Data Migration Checklist: A Starting Point

Creating a robust data migration checklist can be challenging, particularly for those new to the process. To simplify this, we’ve compiled a core set of essential activities for effective data migration planning. This checklist, designed to support thorough preparation for data migration projects, has been successfully used across diverse migration projects over several years, including those for financial institutions (including banks), insurance companies, consulting firms, and other industries. While not exhaustive, it provides a solid foundation that can be customized with project-specific requirements.

It is available for download as a template.

Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

(remove all space from the email account 😊)

Locking Mechanisms in Relational Database Management Systems (RDBMS)

In relational databases, locks are essential mechanisms for managing concurrent access to data. They prevent data corruption and ensure data consistency when multiple transactions try to read or modify the same data simultaneously.

Without locks, concurrent transactions could lead to several problems. For example,

  • Dirty Reads, a transaction may read data that has been modified by another transaction but not yet committed;
  • Lost updates, one transaction’s updates may be overwritten by another transaction;
  • Non-Repeatable Reads, A transaction reads the same data multiple times, and due to updates by other transactions, the results of each read may be different;
  • Phantom Reads: A transaction executes the same query multiple times, and due to insertions or deletions by other transactions, the result set of each query may be different.

Here’s a detailed breakdown of locks in relational databases.

Types of Locks

Relational databases use various types of locks with different levels of restriction:

Shared Lock

Allows multiple read operations simultaneously. Prevents write operations until the lock is released.

Example: SELECT statements in many databases.

Exclusive Lock

Allows a single transaction to modify data. Prevents other operations (read or write) until the lock is released.

Example: UPDATE, DELETE.

Update Lock

Prevents deadlocks when a transaction might upgrade a shared lock to an exclusive lock.

Intent Lock

Indicate the type of lock a transaction intends to acquire. Intent Shared (IS): Intends to acquire a shared lock on a lower granularity level. Intent Exclusive (IX): Intends to acquire an exclusive lock on a lower granularity level.

Lock Granularity

Locks can be applied at different levels of granularity.

Row-Level Lock

Locks a specific row in a table. Provide the highest concurrency, but if many rows are locked, it may lead to lock management overhead.
Example: Updating a specific record (UPDATE ... WHERE id = 1).

Page-Level Lock

Locks a data page, a block of rows. Provide a compromise between concurrency and overhead.

(a page is a fixed-size storage unit)

Table-Level Lock

Locks an entire table. Provide the lowest concurrency but minimal overhead.

Example: Prevents any modifications to the table during an operation like ALTER TABLE.

Lock Duration

Transaction Locks: Held until the transaction is committed or rolled back.

Session Locks: Held for the duration of a session.

Temporary Locks: Released immediately after the operation completes.

Deadlocks Prevention and Handling

A deadlock occurs when two or more transactions are waiting for each other to release locks. Databases employ deadlock detection and resolution mechanisms to handle such situations.

Prevent Deadlocks

Avoid Mutual Exclusion
Use resources that allow shared access (e.g., shared locks for read-only operations).

 Eliminate Hold and Wait
Require transactions to request all resources they need at the beginning. If any resource is unavailable, the transaction must wait without holding any resources.

Allow Preemption
If a transaction requests a resource that is held by another, the system can preempt (forcefully release) the resource from the holding transaction. The preempted transaction is rolled back and restarted.

Break Circular Wait
Impose a global ordering on resources and require transactions to request resources in that order. For example, if resources are ordered as R1, R2, R3, a transaction must request R1 before R2, and R2 before R3.

    Handle Deadlocks

    If deadlocks cannot be prevented, the database system must detect and resolve them. Here’s how deadlocks are typically handled:

    Deadlock Detection
    The database system periodically checks for deadlocks by analyzing the wait-for graph, which represents transactions and their dependencies on resources. If a cycle is detected in the graph, a deadlock exists.

    Deadlock Resolution
    Once a deadlock is detected, the system must resolve it by choosing a victim transaction to abort. The victim is typically selected based on criteria such as:

    • Transaction Age: Abort the newest or oldest transaction.
    • Transaction Progress: Abort the transaction that has done the least work.
    • Priority: Abort the transaction with the lowest priority.

    The aborted transaction is rolled back, releasing its locks and allowing other transactions to proceed.

    Conclusion

    Locks are crucial for ensuring data consistency and integrity in relational databases. Understanding the different types of locks, lock granularity, locking protocols, and isolation levels is essential for database developers and administrators to design and manage concurrent applications effectively.

    Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

    (remove all space from the email account 😊)

    Comprehensive migration engineering strategy

    What is Data Migration

    In general, data migration is the process of moving digital information. These projects are often initiated due to various reasons, such as upgrading databases, deploying new applications, or migrating from on-premises to cloud-based environments. The migration process typically involves preparing, extracting, transforming, and loading the data, usually as a one-time effort.

    Data Migration Types

    Migration types refer to the various strategies used to move databases, storage, applications, business processes, and cloud environments. Common migration types are described below. The specific type of data migration undertaken depends on business requirements.

    Database migration 

    Database migration can refer to either moving data from one database vendor to another, or to upgrading your database software to a newer version. The data format can vary between vendors so a transformation process may be required. In most cases, a change in database technology should not affect the application layer but you should definitely test to confirm.

    Storage migration 

    Storage migration involves transferring data from an existing repository to another, often new repository. The data usually remains unchanged during a storage migration. The goal is typically to upgrade to more modern technology which scales data more cost-effectively and processes data faster.

    Business process migration 

    Business process migration involves the transfer of databases and applications containing data related to customers, products, and operations. Data often requires transformation as it moves from one data model to another. These projects are usually triggered by a company reorganization, merger or acquisition.

    Application migration 

    Application migration refers to moving a software application such as an ERP or CRM system from one computing environment to another. The data usually requires transformation as it moves from one data model to another. This process most often occurs when the company decides to change to a new application vendor and/or involves transferring from on-premises to a public cloud or moving from one cloud to another.

    Cloud migration 

    Cloud migration is a frequently discussed aspect of data migration, encompassing the movement of data, applications, and other business components to a cloud computing environment, including cloud data warehouses. This migration can occur from an on-premises data center to a public cloud, or between different cloud platforms. The reverse process, known as “cloud exit,” involves migrating data and applications from the public cloud back to an on-premises infrastructure.

    Common Data Migration Challenges

    Due to the criticality of data to organizational operations, data migration is a complex process necessitating thorough risk assessment. Numerous challenges frequently arise during implementation. The following are some of the most common data migration challenges.

    • Data Loss: Data Loss: Incomplete data transmission can occur. This can result in irrevocable data loss.
    • Semantics errors: Data migration can lead to semantic errors, where the meaning or interpretation of data changes. For instance, if a source field called “grand total” is migrated to a different field or column in the target system, the data’s intended meaning is lost or distorted.
    • Extended downtime: If the migration process takes longer than anticipated, it can lead to significant disruptions and losses for your business.
    • Data corruption: Migrating unwanted data types can corrupt the target system. This can lead to system crashes or damage the data organization.
    • Performance: Performance issues can stem from poor code quality, application bugs, or an inability to handle high workloads.
    • Orchestration: Orchestration refers to the organized migration of disparate data from multiple sources to a unified location. Inadequate data migration planning can lead to the unintended creation of new data silos by failing to maintain proper tracking of data points. This issue is compounded when multiple disconnected teams operate within different departments or when functional and technical teams utilize data in a variety of ways.
    • Integration: Integrating data sources with other tools and systems allows for data sharing. However, improper integration can lead to the loss of valuable insights.
    • User training: Data migration necessitates a shift in staff focus from existing systems to a new platform. Without adequate training on the new system, users are more prone to making errors.
    • Data security: Data migration introduces significant security risks, including potential exposure to third parties and the possibility of migrating to a more vulnerable system.
    • Data quality: Poor data quality, including missing, inconsistent, useless, or incorrect data, can have significant negative consequences when migrated. These consequences include reduced target system performance, bugs, and system errors.

    Not only above mentioned challenges, but business continuity and costs are common faced challenges.

    • Business continuity: To ensure a positive user experience during data migration, minimize service disruption. When downtime or slowdowns are unavoidable, schedule migrations during off-peak hours and provide clear, timely communication to users through multiple channels, including email, in-app notifications, and social media.
    • Costs: Data migration involves various expenses, including tools, human resources, new infrastructure, and decommissioning costs for old infrastructure. Thorough budgeting is essential before starting the process. Factor in potential productivity and revenue losses due to downtime. Minimizing outages and proactive user communication can help control migration costs.

    Common migration strategy

    Several common strategies are employed for data migration, which is the process of moving data between platforms. These include:

    Big Bang data migration

    In a Big Bang migration, all data assets are moved from the source environment to the target environment in a single, comprehensive operation within a relatively short window of time. This approach necessitates system downtime during the data transfer and transformation process to ensure compatibility with the target infrastructure.

    Advantages: less costly, less complex, takes less time, all changes happen once

    Disadvantages: a high risk of expensive failure, requires downtime

    The big bang approach fits small companies or businesses working with small amounts of data. It doesn’t work for mission-critical applications that must be available 24/7.

    Trickle migration

    Trickle Migration (also known as phased or iterative migration): This strategy divides the overall migration process into smaller, manageable sub-migrations, each with its own specific objectives, timelines, scope, and quality assurance measures. By operating the old and new systems concurrently and migrating data in small increments, trickle migration achieves near-zero downtime, maintaining uninterrupted application availability for users.

    Advantages: less prone to unexpected failures, zero downtime required

    Disadvantages: more expensive, takes more time, needs extra efforts and resources to keep two systems running

    Trickle migration is the right choice for medium and large enterprises that can’t afford long downtime but have enough expertise to face technological challenges.

    Comparison of Migration strategy

    Feature/AspectTrickle MigrationBig Bang Migration
    DefinitionData and systems are migrated incrementally, in smaller phases, over time.All data and systems are migrated in one large, single event.
    ApproachIterative and gradual.One-time, all-at-once migration.
    TimelineExtended, as it spans multiple phases or iterations.Shorter, focused on a single migration window.
    RiskLower risk due to phased testing and gradual changes.Higher risk because everything changes at once.
    ComplexityMore complex due to managing coexistence of old and new systems.Simpler as there’s no coexistence of systems.
    DowntimeMinimal downtime per phase, but over a longer time overall.Typically involves a significant downtime window.
    TestingEasier to test in smaller chunks.Requires comprehensive pre-migration testing.
    User ImpactLower immediate impact, users can transition gradually.High immediate impact, users must adapt quickly.
    CostPotentially higher due to prolonged migration and dual operations.Lower due to single-event focus but risks unforeseen costs from errors.
    SuitabilityBest for large, complex systems with critical operations needing minimal disruptions.Best for smaller, less complex systems or when speed is a priority.

    Migration Process

    Data migration projects, due to their involvement with critical data and potential impact on stakeholders, present inherent challenges. Prior to any data transfer, a robust and well-defined migration plan is a necessity. A successful data migration initiative is predicated on an initial, comprehensive analysis and assessment of the data’s lifecycle. Irrespective of the specific methodology employed, all data migration projects adhere to a consistent set of key phases.

    Stage 1: Project Planning

    Prior to commencing the migration process, it is imperative to establish well-defined objectives and delineate the scope of the data migration. This process involves determining the precise data set required for transfer, including the identification and exclusion of obsolete records. Furthermore, potential compatibility issues between the source and target environments must be addressed, particularly in cases involving migration between disparate database paradigms, such as from a relational database (e.g., Oracle) to a non-relational database (e.g., MongoDB).

    This initial phase involves follow key steps:

    1.1. Define clear and measurable objectives

    Define clear and measurable objectives for the data migration project, including specifying the precise data to be migrated, defining success criteria.

    1.2. Refine the project scope

    Define the precise scope of the data migration by identifying and excluding all non-essential data elements, focusing solely on the minimum dataset necessary to ensure effective target system operation. This process necessitates a high-level comparative analysis of the source and target systems, conducted in consultation with the end-users directly impacted by the migration.

    1.3. Risk assessment

    A comprehensive risk assessment is conducted to identify potential challenges and roadblocks that could impede the data migration project. This assessment includes evaluating potential impacts on the organization and developing mitigation strategies for contingencies such as data loss, downtime, or other failures.

    1.4. Estimate the budget and set realistic timelines

    Subsequent to scope refinement and system evaluation, the appropriate migration methodology (e.g., Big Bang or Trickle) is selected, resource requirements are estimated, and a realistic project timeline is defined. It should be noted that enterprise-scale data migration projects typically require a duration of six months to two years.

    Stage 2: Discovery and Profiling

    This initial phase of the data migration methodology involves a comprehensive assessment of the data landscape. This assessment encompasses data inventory, analysis, auditing, and profiling to thoroughly examine and cleanse the data set targeted for migration. The objective is to identify and address potential data conflicts, detect and remediate data quality issues, and eliminate redundant or anomalous data elements prior to the commencement of the migration process.

    2.1. Source System Assessment

    2.1.1. Identify Data Sources
    • Primary Sources: Identify the primary sources of data, such as databases, files, APIs, etc.
    • Secondary Sources: Identify any secondary or external data sources that may need to be migrated.
    2.1.2. Understand the Data Structure
    • Data Models: Review the data models, schemas, and relationships between different data entities.
    • Data Types: Identify the types of data (e.g., text, numeric, date, binary) and their formats.
    • Data Volume: Estimate the volume of data to be migrated, including the number of records, tables, and databases.
    • Data Quality: Assess the quality of the data, including issues like duplicates, missing values, and inconsistencies.
    2.1.3. Analyze Data Dependencies
    • Interdependencies: Identify relationships and dependencies between different data entities.
    • Business Rules: Understand any business rules or logic applied to the data in the source system.
    • Data Flow: Map out how data flows through the source system, including ETL (Extract, Transform, Load) processes.
    2.1.4. Evaluate Data Security and Compliance
    • Access Controls: Review who has access to the data and what permissions they have.
    • Encryption: Check if data is encrypted at rest or in transit.
    • Compliance: Ensure the data complies with relevant regulations (e.g., GDPR, HIPAA).
    2.1.5. Document Source System
    • Metadata: Document metadata, including data definitions, formats, and constraints.
    • Data Dictionary: Create or update a data dictionary that describes the data elements in the source system.

    2.2. Target System Assessment

    2.2.1. Understand the Target System Architecture
    • Data Models: Review the data models and schemas of the target system.
    • Data Types: Ensure the target system supports the data types and formats used in the source system.
    • Storage Capacity: Verify that the target system has sufficient storage capacity for the migrated data.
    2.2.2. Evaluate Data Transformation Requirements
    • Data Mapping: Map data fields from the source system to the target system.
    • Data Transformation: Identify any transformations needed to convert data from the source format to the target format.
    • Data Validation: Plan for data validation to ensure accuracy and completeness after migration.
    2.2.3. Assess System Performance
    • Performance Benchmarks: Evaluate the performance of the target system to ensure it can handle the volume and complexity of the migrated data.
    • Scalability: Ensure the target system can scale to accommodate future data growth.
    2.2.4. Review Security and Compliance
    • Access Controls: Ensure the target system has appropriate access controls in place.
    • Encryption: Verify that data will be encrypted at rest and in transit in the target system.
    • Compliance: Ensure the target system complies with relevant regulations.
    2.2.5. Test the Target System
    • Test Environment: Set up a test environment that mirrors the target system.
    • Pilot Migration: Perform a pilot migration to test the process and identify any issues.
    • User Acceptance Testing (UAT): Conduct UAT to ensure the migrated data meets user requirements.

    2.3. Comparative Analysis of Source and Target Systems

    2.3.1. Network and Connectivity
    • Confirm bandwidth, latency, and reliability between source and target systems.
    • Address firewall or VPN requirements for data flow.
    2.3.2. Data Transformation Needs

    Determine if data needs cleansing, enrichment, or reformatting during migration.
    Plan for ETL (Extract, Transform, Load) processes if required.


    2.3.3. Testing Environments

    Establish sandbox or test environments in both systems for validation.


    2.3.4. Documentation and Communication

    Document findings and share with stakeholders to align expectations.
    Maintain clear communication between teams managing source and target systems.

    Stage 3: Resource Allocation and Solution Development

    For large data assets, a phased development approach is recommended, wherein the data is segmented, and the migration logic is developed and tested iteratively for each segment.

    3.1 Set data standards

    This will allow your team to spot problem areas across each phase of the migration process and avoid unexpected issues at the post-migration stage.

    3.2 Architecture Design and Resource Allocation

    This phase encompasses both the design of the migration architecture and the allocation of necessary resources. It is imperative to confirm the availability and commitment of all requisite resources, including internal personnel, external consultants, vendors, and enabling technologies. This verification extends to resources required for post-migration activities, such as user training and communication. Upon confirmation of resource availability, the development of the migration logic commences, encompassing the processes of data extraction, transformation, and loading (ETL) into the designated target repository.

    3.3 Create a Detailed Migration Plan
    • Data Extraction: Plan for data extraction from the source system.
    • Data Transformation: Outline the steps for data transformation.
    • Data Loading: Plan for loading data into the target system.
    • Testing: Include testing phases in the migration plan.

    stage 4: Backup and Contingency Planning

    Despite careful planning, data migration projects can face unexpected challenges. A robust backup strategy is essential to ensure data can be recovered and systems remain operational in the event of unforeseen issues during the migration process. Furthermore, detailed contingency plans should be developed to address each identified potential setback or roadblock.

    stage 5: Execution

    5.1. Pre-migration – sampling testing

    To assess the accuracy of the migration and identify any potential data quality issues, test the migration process using a representative data sample.

    5.2. User Acceptance Testing (UAT)

    User Acceptance Testing (UAT) is a critical phase in the data migration process where end-users validate that the migrated data and system meet their business requirements and expectations. UAT ensures that the migration solution works as intended in a real-world scenario before it is fully deployed. we should focus on business goals and customer satisfaction.

    5.3. Executing the Migration Solution

    Following successful completion of testing procedures, the data migration process, encompassing data extraction, transformation, and loading (ETL), is formally initiated. In a Big Bang migration scenario, the execution phase is typically completed within a period of several days. Conversely, the Trickle migration methodology employs an incremental data transfer approach, resulting in a more protracted execution timeline but significantly mitigating the risk of critical system failures and minimizing downtime.

    stage 6: Documentation and Reporting

    After completing a data migration, documentation and reporting are critical steps to ensure the process is well-documented, auditable, and provides insights for future improvements. Proper documentation and reporting help stakeholders understand the migration’s success, identify any issues, and maintain a record for compliance and reference purposes.

    6.1. Documentation

    Documentation provides a detailed record of the data migration process, including the steps taken, decisions made, and outcomes. It serves as a reference for future migrations, audits, or troubleshooting.

    Key Components of Documentation

    1. Migration Plan:
      • Include the original migration plan, including objectives, scope, timelines, and resource allocation.
    2. Data Mapping:
      • Document the mapping of source data fields to target data fields.
      • Include any transformations or conversions applied during the migration.
    3. Data Validation:
      • Record the validation rules and checks performed to ensure data accuracy and completeness.
      • Include sample validation results and any discrepancies found.
    4. Error Handling:
      • Document any errors encountered during the migration and how they were resolved.
      • Include a log of rejected or failed records and the reasons for rejection.
    5. Migration Tools and Scripts:
      • Provide details of the tools, scripts, or software used for the migration.
      • Include version numbers, configurations, and any custom code.
    6. Testing Results:
      • Document the results of pre-migration testing, including unit tests, integration tests, and user acceptance tests (UAT).
      • Include test cases, expected outcomes, and actual results.
    7. Post-Migration Verification:
      • Record the steps taken to verify the success of the migration.
      • Include checks for data integrity, completeness, and performance in the target system.
    8. Lessons Learned:
      • Summarize what went well and what could be improved in future migrations.
      • Include feedback from the migration team and stakeholders.
    9. Compliance and Security:
      • Document compliance with relevant regulations (e.g., GDPR, HIPAA).
      • Include details of security measures taken during the migration.
    10. Rollback Plan:
      • Document the rollback plan and whether it was executed (if applicable).
      • Include details of any fallback procedures used.
    6.2. Reporting

    Reporting provides a summary of the migration process and outcomes for stakeholders. It highlights key metrics, successes, and areas for improvement.

    Key Components of Reporting

    • Executive Summary:
      • Provide a high-level overview of the migration, including objectives, scope, and outcomes.
      • Highlight key achievements and challenges.
    • Migration Metrics:
      • Include quantitative metrics such as:
        • Volume of data migrated (e.g., number of records, tables, databases).
        • Time taken for the migration.
        • Number of errors or rejected records.
        • Downtime (if applicable).
    • Data Quality Report:
      • Summarize the results of data validation and quality checks.
      • Include metrics such as:
        • Percentage of accurate records.
        • Percentage of incomplete or duplicate records.
        • Number of records requiring manual intervention.
    • Performance Report:
      • Compare the performance of the target system before and after migration.
      • Include metrics such as:
        • Response times.
        • Throughput.
        • System uptime.
    • Issue and Risk Log:
      • Provide a summary of issues encountered during the migration and how they were resolved.
      • Include a risk assessment and mitigation strategies.
    • Stakeholder Feedback:
      • Summarize feedback from stakeholders, including end-users, IT teams, and business leaders.
      • Highlight any concerns or suggestions for improvement.
    • Post-Migration Support:
      • Document the support provided after the migration, including:
        • Troubleshooting and issue resolution.
        • User training and documentation.
        • Monitoring and maintenance activities.
    • Recommendations:
      • Provide recommendations for future migrations or system enhancements.
      • Include best practices and lessons learned.

    stage 7: Post-Migration Assessment Validating, Auditing and Monitor 

    7.1. Post-migration Validation and Auditing.

    Once the migration is complete, perform post-migration validation to verify that all data is accurately transferred and that the new system functions as expected. Conduct regular audits to ensure data integrity and compliance with data regulations.

    7.2. User Training and Communications

    User Training and Communications, Ongoing stakeholder communications is crucial throughout the data migration process. This should include keeping everyone informed about the migration schedule, potential disruptions, and expected outcomes, as well as providing end-user training/instructions to smooth the transition and prevent any post-migration usability issues.
    Once the migration is complete, perform post-migration validation to verify that all data is accurately transferred and that the new system functions as expected. Conduct regular audits to ensure data integrity and compliance with data regulations.

    7.3. Continuous Performance Monitoring

    Ongoing monitoring of the new system’s performance is vital for surfacing any post-migration data loss and/or data corruption issues. Regularly assess the target system’s performance and investigate any potential data-related performance bottlenecks/issues.

    7.4. Data Security and Compliance

    Last but certainly not least, ensure that data security and compliance requirements are met during and after the migration process. This may include implementing data encryption at rest and in transit, access controls, and data protection measures to safeguard sensitive information.

    Conclusion

    Assessing the source and target systems is a foundational step in ensuring a successful data migration. By thoroughly evaluating both systems, identifying potential risks, and developing a comprehensive migration plan, you can minimize disruptions and ensure that the migrated data is accurate, secure, and compliant with relevant regulations.

    Sticking to the best practices can increase the likelihood of successful data migration. each data migration project is unique and presents its own challenges, the following golden rules may help companies safely transit their valuable data assets, avoiding critical delays.

    Use data migration as an opportunity to reveal and fix data quality issues. Set high standards to improve data and metadata as you migrate them.

    Hire data migration specialists and assign a dedicated team to run the project.

    Minimize the amount of data for migration.

    Profile all source data before writing mapping scripts.

    Allocate considerable time to the design phase as it greatly impacts project success.

    Don’t be in a hurry to switch off the old platform. Sometimes, the first attempt at data migration fails, demanding rollback and another try.

    Data migration is often viewed as a necessary evil rather than a value-adding process. This seems to be the key root of many difficulties. Considering migration an important innovation project worthy of special focus is half the battle won.

    Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

    (remove all space from the email account 😊)

    Comparison of Azure SQL Managed Instance, Azure SQL Database, Azure SQL Server

    Azure offers several SQL-related services, each tailored to different use cases and requirements. Below is a comparison of Azure SQL Managed InstanceAzure SQL Database, and Azure SQL Server (often referred to as a logical SQL Server in Azure).

    Azure SQL Database

    1. Azure SQL Database

    • Description: A fully managed, platform-as-a-service (PaaS) relational database offering. It is designed for modern cloud applications and supports single databases and elastic pools.
    • Use Cases:
      • Modern cloud-native applications.
      • Microservices architectures.
      • Applications requiring automatic scaling, high availability, and minimal management overhead.
    • Key Features:
      • Single database or elastic pools (shared resources for multiple databases).
      • Automatic backups, patching, and scaling.
      • Built-in high availability (99.99% SLA).
      • Serverless compute tier for cost optimization.
      • Limited SQL Server surface area (fewer features compared to Managed Instance).
    • Limitations:
      • No support for SQL Server Agent, Database Mail, or cross-database queries.
      • Limited compatibility with on-premises SQL Server features.
    • Management: Fully managed by Microsoft; users only manage the database and its resources.

    Azure SQL Managed Instance

    • Description: A fully managed instance of SQL Server in Azure, offering near 100% compatibility with on-premises SQL Server. It is part of the PaaS offering but provides more control and features compared to Azure SQL Database.
    • Use Cases:
      • Lift-and-shift migrations of on-premises SQL Server workloads.
      • Applications requiring full SQL Server compatibility.
      • Scenarios needing features like SQL Server Agent, cross-database queries, or linked servers.
    • Key Features:
      • Near 100% compatibility with SQL Server.
      • Supports SQL Server Agent, Database Mail, and cross-database queries.
      • Built-in high availability (99.99% SLA).
      • Virtual network (VNet) integration for secure connectivity.
      • Automated backups and patching.
    • Limitations:
      • Higher cost compared to Azure SQL Database.
      • Slightly longer deployment times.
      • Limited to a subset of SQL Server features (e.g., no Windows Authentication).
    • Management: Fully managed by Microsoft, but users have more control over instance-level configurations.

    Azure SQL Server

    Description: A logical server in Azure that acts as a central administrative point for Azure SQL Database and Azure SQL Managed Instance. It is not a standalone database service but rather a management layer.

    Use Cases:

    • Managing multiple Azure SQL Databases or Managed Instances.
    • Centralized authentication and firewall rules.
    • Administrative tasks like setting up logins and managing access.

    Key Features:

    • Acts as a gateway for Azure SQL Database and Managed Instance.
    • Supports Azure Active Directory (AAD) and SQL authentication.
    • Configurable firewall rules for network security.
    • Provides a connection endpoint for databases.

    Limitations:

    • Not a database service itself; it is a management tool.
    • Does not host databases directly.

    Management: Users manage the server configuration, logins, and firewall rules.

    Side by side Comparison 

    Feature/AspectAzure SQL DatabaseAzure SQL Managed InstanceAzure SQL Server (Logical)
    Service TypeFully managed PaaSFully managed PaaSManagement layer
    CompatibilityLimited SQL Server featuresNear 100% SQL Server compatibilityN/A (management tool)
    Use CaseCloud-native appsLift-and-shift migrationsCentralized management
    High Availability99.99% SLA99.99% SLAN/A
    VNet IntegrationLimited (via Private Link)SupportedN/A
    SQL Server AgentNot supportedSupportedN/A
    Cross-Database QueriesNot supportedSupportedN/A
    CostLowerHigherFree (included in service)
    Management OverheadMinimalModerateMinimal

    SQL Server’s Side-by-Side Feature: Not Available in Azure SQL

    Following are list that shows SQL Server have but not available in Azure SQL Database and Azure SQL Managed Instance.

    1. Instance-Level Features

    FeatureSQL ServerAzure SQL DatabaseAzure SQL Managed Instance
    Multiple Databases Per Instance✅ Full support❌ Only single database per instance✅ Full support
    Cross-Database Queries✅ Full support❌ Limited with Elastic Query✅ Full support
    SQL Server Agent✅ Full support❌ Not available✅ Supported (with limitations)
    PolyBase✅ Full support❌ Not available❌ Not available
    CLR Integration (SQL CLR)✅ Full support❌ Not available✅ Supported (with limitations)
    FileStream/FileTable✅ Full support❌ Not available❌ Not available

    2. Security Features

    FeatureSQL ServerAzure SQL DatabaseAzure SQL Managed Instance
    Database Mail✅ Full support❌ Not available❌ Not available
    Service Broker✅ Full support❌ Not available❌ Not available
    Custom Certificates for Transparent Data Encryption (TDE)✅ Full support❌ Limited to Azure-managed keys❌ Limited customization

    3. Integration Services

    FeatureSQL ServerAzure SQL DatabaseAzure SQL Managed Instance
    SSIS Integration✅ Full support❌ Requires external tools❌ Requires external tools
    SSRS Integration✅ Full support❌ Not available❌ Not available
    SSAS Integration✅ Full support❌ Not available❌ Not available

    4. Specialized Features

    FeatureSQL ServerAzure SQL DatabaseAzure SQL Managed Instance
    Machine Learning Services (R/Python)✅ Full support❌ Not available❌ Not available
    Data Quality Services (DQS)✅ Full support❌ Not available❌ Not available

    Conclusion

    • Azure SQL Database: Ideal for new cloud-native applications or applications that don’t require full SQL Server compatibility.
    • Azure SQL Managed Instance: Best for migrating on-premises SQL Server workloads to the cloud with minimal changes.
    • Azure SQL Server (Logical): Used for managing and administering Azure SQL Databases and Managed Instances.

    Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

    (remove all space from the email account 😊)

    DBFS: Databricks File System (DBFS)

    The Databricks File System (DBFS) is a distributed file system integrated with Databricks that allows users to interact with object storage systems like Azure Blob Storage, Amazon S3, and Google Cloud Storage. DBFS enables seamless access to these cloud storage systems within Databricks notebooks and clusters, appearing like a local file system.

    Databricks recommends that you store data in mounted object storage rather than in the DBFS root. The DBFS root is not intended for production customer data.

    DBFS root is the default file system location provisioned for a Databricks workspace when the workspace is created. It resides in the cloud storage account associated with the Databricks workspace.

    Key Features of DBFS

    • Unified Storage Access: DBFS provides a unified interface to interact with various cloud storage platforms (Azure Blob, S3, etc.)
    • Mounting External Storage: DBFS allows you to mount cloud storage containers or buckets so that they are accessible from your Databricks environment like a directory.
    • Persistence: Files written to DBFS in certain directories are persistent and accessible across clusters, ensuring that data is stored and available even when clusters are shut down
    • Interoperability: DBFS integrates with Databricks’ Spark engine, meaning you can read and write data directly into Spark DataFrames,

    Structure of DBFS

    The Databricks File System is structured similarly to a Unix-like file system. It has the following key components:

    • /FileStore: This is the default directory where you can upload and store small files, such as libraries, scripts, and other assets.
    • /databricks-datasets: This directory contains sample datasets provided by Databricks for learning purposes.
    • /mnt: This is the mount point for external cloud storage, where you can mount and interact with cloud storage services like Azure Blob, AWS S3, or GCS (Google Cloud Storage).

    Working with DBFS

    List Files in DBFS

    dbutils.fs.ls(“/FileStore/”)

    Upload Files

    dbutils.fs.put(“/FileStore/my_file.txt”, “Hello, DBFS!”, overwrite=True)

    Reading Files

    df = spark.read.csv(“/FileStore/my_file.csv”, header=True, inferSchema=True)

    Writing Files

    df.write.csv(“/FileStore/my_output.csv”, mode=”overwrite”)

    Mounting External Storage

    dbutils.fs.mount(
      source = "wasbs://<container>@<storage-account-name>.blob.core.windows.net",
      mount_point = "/mnt/myblobstorage",
      extra_configs = {"<storage-account-name>.blob.core.windows.net":dbutils.secrets.get(scope = "<scope-name>", key = "<storage-access-key>")})
    

    Unmounting Storage

    dbutils.fs.unmount(“/mnt/myblobstorage”)

    Conclusion

    The Databricks File System (DBFS) is a crucial feature in Databricks that provides seamless, scalable file storage and cloud integration. It abstracts away the complexity of working with distributed storage systems, making it easy to manage and process data. With capabilities like mounting external storage, integration with Spark, and support for various file formats, DBFS is an essential tool for any data engineering or analytics workflow within Databricks.

    Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

    (remove all space from the email account 😊)

    delta: Schema Evolution

    Schema Evolution in Databricks refers to the ability to automatically adapt and manage changes in the structure (schema) of a Delta Lake table over time. It allows users to modify the schema of an existing table (e.g., adding or updating columns) without the need for a complete rewrite of the data.

    Key Features of Schema Evolution

    1. Automatic Adaptation: Delta Lake can automatically evolve the schema of a table when new columns are added to the incoming data, or when data types change, if certain configurations are enabled.
    2. Backward and Forward Compatibility: Delta Lake ensures that new data can be written to a table without breaking the existing schema. It also ensures that existing queries remain compatible, even if the schema changes.

    Configuration for Schema Evolution

    mergeSchema

    This option allows you to append new data with a schema that differs from the existing table schema. It merges the new schema into the table.

    Usage: Typically used when you are appending data.

    Schema Merging: Use mergeSchema only for adding new columns, not for incompatible changes.

    When new data has additional columns that aren’t present in the target Delta table, Delta Lake can automatically merge the new schema into the existing table schema.

    
    # Append new data to the Delta table with automatic schema merging
    
    df_new_data.write.format("delta").mode("append").option("mergeSchema", "true").save("/path/to/delta-table")
    
    
    

    overwriteSchema

    This option is used when you want to completely replace the schema of the table with the schema of the new data.

    If you want to replace the entire schema (including removing existing columns), you can use the overwriteSchema option.

    
    # Overwrite the existing Delta table schema with new data
    
    df_new_data.write.format("delta").mode("overwrite").option("overwriteSchema", "true").save("/path/to/delta-table")
    
    
    

    Configure spark.databricks.delta.schema.autoMerge

    You can configure this setting at the following levels:

    Usage: Typically used when you are overwriting data

    • Session Level (applies to a specific session or job)
    • Cluster Level (applies to all jobs on the cluster)

    Session-Level Configuration (Spark session level)

    Once this is enabled, all write and merge operations in the session will automatically allow schema evolution.

    
    # Enable auto schema merging for the session
    
    spark.conf.set("spark.databricks.delta.schema.autoMerge.enabled", "true")
    
    

    Cluster-Level Configuration

    This enables automatic schema merging for all operations on the cluster without needing to set it in each job.

    1. Go to your Databricks Workspace.
    2. Navigate to Clusters and select your cluster.
    3. Go to the Configuration tab.
    4. Under Spark Config, add the following configuration:
      spark.databricks.delta.schema.autoMerge.enabled true

    Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

    (remove all space from the email account 😊)

    Delta Table, Delta Lake

    A Delta table is a type of table that builds on the Delta Lake storage layer and brings ACID (Atomicity, Consistency, Isolation, Durability) transactions, schema enforcement, and scalable metadata management to traditional data lakes. It is designed for large-scale, reliable data processing and analytics. Delta tables enable you to manage both batch and streaming data with ease, and they are ideal for environments where data integrity and consistency are critical, such as in data lakes, data warehouses, and machine learning pipelines.

    What is Delta Lake

    Delta lake is an open-source technology, we use Delta Lake to store data in Delta tables. Delta lake improves data storage by supporting ACID transactions, high-performance query optimizations, schema evolution, data versioning and many other features.

    FeatureTraditional Data LakesDelta Lake
    Transaction SupportNo ACID transactionsFull ACID support
    Data ConsistencyWeak guaranteesStrong guarantees with serializable isolation
    Schema EnforcementNoneEnforced and allows schema evolution
    Handling StreamingRequires separate infrastructureUnified batch and streaming
    Data ManagementProne to issues like data corruptionReliable with audit trails and versioning
    key differences

    There is detail information at “Data lake vs delta lake vs data lakehouse, and data warehouses comparison

    Key Features of Delta Tables

    1. ACID Transactions: Delta Lake ensures that operations like reads, writes, and updates are atomic, consistent, isolated, and durable, eliminating issues of partial writes and data corruption.
    2. Schema Enforcement: When writing data, Delta ensures that it matches the table’s schema, preventing incorrect or incomplete data from being written.
    3. Time Travel: Delta tables store previous versions of the data, which allows you to query, rollback, and audit historical data (also known as data versioning).
    4. Unified Streaming and Batch Processing: Delta tables allow you to ingest both batch and streaming data, enabling you to work seamlessly with either approach without complex rewrites.
    5. Efficient Data Upserts: You can perform MERGE operations (UPSERTS) efficiently, which is especially useful in scenarios where you need to insert or update data based on certain conditions.
    6. Optimized Performance: Delta Lake supports optimizations such as data skipping, Z-order clustering, and auto-compaction, improving query performance.

    Using Delta Tables in PySpark or SQL

    If we directly query a existing delta table from ADLS using SQL, always use

     --back single quotation mark `
    delta.`abfss://contain@account.dfs.windows.net/path_and_table`
    

    Register, Create, Write a Delta table

    Register a table point it to existing Delta table location

    # sql
    -- register a table point it to existing Delta table location
    delta_table_path = "dbfs:/mnt/delta/table_path"
    # Register the Delta table in the metastore
    spark.sql(f"""
    CREATE TABLE table_name
    USING DELTA
    LOCATION '{delta_table_path}'
    """)

    Creating a Delta Table

    -- Creating a Delta Table
    %sql
    CREATE TABLE my_delta_table (
    id int,
    name string
    )
    USING delta
    LOCATION '/mnt/delta/my_delta_table';

    Write to delta table

    # python
    # Write a DataFrame to a Delta table
    df.write.format("delta").save("/mnt/delta/my_delta_table")
    
    # sql
    -- Insert data
    INSERT INTO my_delta_table VALUES (1, 'John Doe'), (2,
    'Jane Doe');

    Reading from a Delta table

    
    #python
    delta_df = spark.read.format("delta").load("/mnt/delta/my_delta_table")
    delta_df.show()
    
    
    #sql
    -- Query Delta table
    SELECT * FROM my_delta_table;
    
    -- directly query delta table from adls.
    -- use  ` back single quotation mark
    SELECT * 
    FROM 
    delta.`abfss://adlsContainer@adlsAccount.dfs.windows.net/Path_and_TableName`
    VERSION AS OF 4;
    
    

    Managing Delta Tables

    Optimizing Delta Tables

    To improve performance, you can run an optimize operation to compact small files into larger ones.

    # sql 
    OPTIMIZE my_delta_table;

    Z-order Clustering

    Z-order clustering is used to improve query performance by colocating related data in the same set of files. it is a technique used in Delta Lake (and other databases) to optimize data layout for faster query performance.

    # sql
    OPTIMIZE my_delta_table ZORDER BY (date);

    Upserts (Merge)

    Delta Lake makes it easy to perform Upserts (MERGE operation), which allows you to insert or update data in your tables based on certain conditions.

    using SQL scripts is the same as TSQL merge statement

    % sql
    MERGE INTO my_delta_table t
    USING new_data n
    ON t.id = n.id
    WHEN MATCHED THEN UPDATE SET t.value = n.value
    WHEN NOT MATCHED THEN INSERT (id, value) VALUES (n.id, n.value); 

    In PySpark with Delta Lake:

    The target table must be a Delta table and the source data is typically in a DataFrame.

    Example Scenario
    • Target Table: target_table; Contains existing records.
    • Source DataFrame: source_df; Contains new or updated records.
    • Goal: Update existing rows if a match is found or insert new rows if no match exists.
    from delta.tables import DeltaTable
    from pyspark.sql.functions import current_date, lit
    
    # Define paths
    target_table_path = "dbfs:/mnt/delta/target_table"
    
    # Load the Delta table as a DeltaTable object
    target_table = DeltaTable.forPath(spark, target_table_path)
    
    # Source DataFrame (new data to upsert)
    source_data = [
        (1, "Alice", "2023-01-01"),
        (2, "Bob", "2023-01-02"),
        (4, "Eve", "2023-01-04")  # New record
    ]
    columns = ["id", "name", "date"]
    source_df = spark.createDataFrame(source_data, columns)
    
    # Perform the merge operation
    target_table.alias("t").merge(
        source_df.alias("s"),
        "t.id = s.id"  # Join condition: match rows based on `id`
    ).whenMatchedUpdate(
        set={
            "name": "s.name",  # Update `name` column
            "date": "s.date"   # Update `date` column
        }
    ).whenNotMatchedInsert(
        values={
            "id": "s.id",      # Insert `id`
            "name": "s.name",  # Insert `name`
            "date": "s.date"   # Insert `date`
        }
    ).execute()
    
    # Verify the result
    result_df = spark.read.format("delta").load(target_table_path)
    result_df.show()
    
    Explanation of the Code
    1. Target Table (target_table):
      • The Delta table is loaded using DeltaTable.forPath.
      • This table contains existing data where updates or inserts will be applied.
    2. Source DataFrame (source_df):
      • This DataFrame contains new or updated records.
    3. Join Condition ("t.id = s.id"):
      • Rows in the target table (t) are matched with rows in the source DataFrame (s) based on id.
    4. whenMatchedUpdate:
      • If a matching row is found, update the name and date columns in the target table.
    5. whenNotMatchedInsert:
      • If no matching row is found, insert the new record from the source DataFrame into the target table.
    6. execute():
      • Executes the merge operation, applying updates and inserts.
    7. Result Verification:
      • After the merge, the updated Delta table is read and displayed.

    Conclusion

    Delta Lake is a powerful solution for building reliable, high-performance data pipelines on top of data lakes. It enables advanced data management and analytics capabilities with features like ACID transactions, time travel, and schema enforcement, making it an ideal choice for large-scale, data-driven applications.

    Delta tables are essential for maintaining high-quality, reliable, and performant data processing pipelines. They provide a way to bring transactional integrity and powerful performance optimizations to large-scale data lakes, enabling unified data processing for both batch and streaming use cases.

    Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

    (remove all space from the email account 😊)

    Comparison Partitioning Strategies and Methods

    In distributed computing frameworks like Apache Spark (and PySpark), different partitioning strategies are used to distribute and manage data across nodes in a cluster. These strategies influence how data is partitioned, which affects the performance of your jobs. Some common partitioning techniques include hash partitioning, range partitioning, and others like broadcast joins.

    Key Differences Between Partitioning Methods

    Partitioning MethodKey FeatureBest ForShufflingEffect on Data Layout
    partitionBy()
    General Partitioning
     Optimizing data layout on disk (file system)NoOrganizes data into folders by column values
    Hash PartitioningEvenly distributes data based on hash function.Query, such as Joins, groupBy operations, when you need uniform distribution.yesRedistributes data across partitions evenly
    Round RobinSimple, even distribution of rows.Even row distribution without considering valuesYes   Distributes rows evenly across partitions
    Range PartitioningData is divided based on sorted ranges.Queries based on ranges, such as time-series data.Yes (if internal)Data is sorted and divided into ranges across partitions
    Custom PartitioningCustom logic for partitioning.When you have specific partitioning needs not covered by standard methods.Yes (if internal)Defined by custom function
    Co-location of PartitionsPartition both datasets by the same key for optimized joins.Joining two datasets with the same key.No (if already co-located)Ensures both datasets are partitioned the same way
    Broadcast JoinSends smaller datasets to all nodes to avoid shuffles.Joins where one dataset is much smaller than the other.No (avoids shuffle)Broadcasts small dataset across nodes for local join
    Key Differences Between Partitioning Methods

    Key Takeaways

    • partitionBy() is used for data organization on disk, especially when writing out data in formats like Parquet or ORC.
    • Hash Partitioning and Round Robin Partitioning are used for balancing data across Spark

    General Partitioning

    Distributing data within Spark jobs for processing. Use partitionBy() when writing data to disk to optimize data layout and enable efficient querying later.

    
    df.write.format("delta").partitionBy("gender", "age").save("/mnt/delta/partitioned_data")
    
    

    save in this way

    Hash Partitioning

    
    df = df.repartiton(10, 'class_id')
    
    

    Hash partitioning is used internally within Spark’s distributed execution to split the data across multiple nodes for parallel processing. It Splits our data in such way that elements with the same hash (can be key, keys, or a function) will be in the same

    Hash Partitioning Used during processing within Spark, it redistributes the data across partitions based on a hash of the column values, ensuring an even load distribution across nodes for tasks like joins and aggregations. Involves shuffling.

    Round Robin Partitioning

    Round robin partitioning evenly distributes records across partitions in a circular fashion, meaning each row is assigned to the next available partition.

    Range Partitioning

    only it’s based on a range of values.

    Broadcast Join (replication Partitioning)

    Broadcast joins (known as replication partition) in Spark involve sending a smaller dataset to all nodes in the cluster, that means all nodes have the same small dataset or says duplicated small dataset to all nodes. It is allowing each partition of the larger dataset to be joined with the smaller dataset locally without requiring a shuffle.

    Detailed comparison of each partitioning methods

    Partitioning MethodPurposeWhen UsedShufflingHow It Works
    General Partitioning (partitionBy())Organizing data on disk (file partitioning)When writing data (e.g., Parquet, ORC)No shuffleData is partitioned into folders by column values when writing to disk
    Hash Partitioning (repartition(column_name))Evenly distributing data for parallel processingDuring processing for joins, groupBy, etc.Yes (shuffle data across nodes)Applies a hash function to the column value to distribute data evenly across partitions
    Round Robin PartitioningDistributes rows evenly without considering valuesWhen you want even distribution but don’t need value-based groupingYes (shuffle)Rows are evenly assigned to partitions in a circular manner, disregarding content
    Range PartitioningDistribute data into partitions based on a range of valuesWhen processing or writing range-based data (e.g., dates)Yes (if used internally during processing)Data is sorted by the partitioning column and divided into ranges across partitions
    Custom PartitioningApply custom logic to determine how data is partitionedFor complex partitioning logic in special use casesYes (depends on logic)User-defined partitioning function determines partition assignment
    Co-location PartitioningEnsures two datasets are partitioned the same way (to avoid shuffling during joins)To optimize joins when both datasets have the same partitioning columnNo (if already partitioned the same way)Both datasets are partitioned by the same key (e.g., by user_id) to avoid shuffle during joins
    Broadcast Join (Partitioning)Send a small dataset to all nodes for local joins without shuffleWhen joining a small dataset with a large oneNo shuffle (avoids shuffle by broadcasting)The smaller dataset is broadcast to each node, avoiding the need for shuffling large data

    Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

    (remove all space from the email account 😊)

    Azure Data Factory or Synapse Copy Activity with File System

    In Azure Data Factory (ADF) or Synapse, using Copy Activity with a File System as a source or sink is common when dealing with on-premises file systems, network file shares, or even cloud-based file storage systems. Here’s an overview of how it works, key considerations, and steps to set it up.

    Key Components and setup with File System:

    Create a File System Linked Service

    Linked Service: For on-premises or network file systems, you typically need a Self-hosted Integration Runtime (SHIR).

    Fill in the required fields:

    • Connection: Specify the file system type (e.g., network share or local path).
    • Authentication: Provide the appropriate credentials, such as username/password, or key-based authentication.
    • If the file system is on-premises, configure the Self-hosted Integration Runtime to access it.

    Create File System Dataset

    Go to Datasets in ADF and create a new dataset. Select File System as the data source.

    Configure the dataset to point to the correct file or folder:

    • Specify the File Path.
    • Define the file format (e.g., CSV, JSON, XML).
    • Set any schema information if required (for structured data like CSV).

    Considerations:

    • Integration Runtime: For on-premises file systems, the Self-hosted Integration Runtime (SHIR) is essential to securely move data from private networks.
    • Performance: Data transfer speeds depend on network configurations (for on-prem) and ADF’s parallelism settings.
    • File Formats: Ensure proper handling of different file formats (e.g., CSV, JSON, Binary etc.) and schema mapping for structured files.
    • Security: Ensure credentials and network configurations are correctly set up, and consider encryption if dealing with sensitive data.

    Common Errors:

    • Connection issues: If the SHIR is not correctly configured, or if there are issues with firewall or network settings, ADF may not be able to access the file system.
    • Permission issues: Ensure that the correct permissions are provided to access the file system (file share, SMB, FTP, etc.).

    Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

    (remove all space from the email account 😊)

    Comparison between All-Purpose Cluster, Job Cluster, SQL Warehouse and Instance Pools

    side-by-side comparison of “All-Purpose Cluster”, “Job Cluster”, “SQL Warehouse” and Instance Pools in Azure Databricks, covering their key features, use cases, and differences:

    Key Differences

    • All-Purpose Cluster: Best for interactive workloads, collaborative notebooks, and exploration. It stays running until you manually stop it or it hits the idle timeout. Not as cost-effective for long-running or scheduled tasks.
    • Job Cluster: Best for scheduled and automated jobs. It starts automatically when the job begins and shuts down after the job finishes, which makes it cost-efficient and ideal for production ETL or data processing jobs.
    • SQL Warehouse: Best for SQL analytics and BI tool integration. It is specifically optimized for SQL queries, offering auto-scaling based on query load and cost-efficient SQL query execution on Delta Lake tables.
    • Instance Pools: Reducing startup times for frequently created clusters. Sharing resources among multiple teams or clusters.

    Side by side comparison

    All-Purpose ClusterJob ClusterSQL Warehouse (formerly SQL Endpoints)Instance Pools
    PurposeGeneral-purpose compute environment for interactive workloads.Dedicated to run a specific job or task. Automatically terminates after the job.Optimized for running SQL queries, dashboards, and BI analytics on Delta Lake.resource management feature that pre-allocate virtual machines (VMs) to reduce cluster startup times and optimize costs.
    UsageFor interactive development in notebooks, collaboration, and ad-hoc analysis.For scheduled or automated jobs (e.g., ETL tasks) that need to run Spark-based processing.For SQL-based workloads, querying data in Delta Lake, and BI tools (e.g., Power BI, Tableau).Supporting clusters
    Primary WorkloadInteractive development (notebooks, data exploration, ad-hoc queries).Automated Spark jobs with dedicated, isolated clusters for each job.SQL analytics and dashboards, running SQL queries against Delta Lake tables.Resource optimization
    Cluster LifecycleRemains active until manually terminated or idle timeout is reached.Created automatically when a job is triggered, and terminated when the job is done.SQL Warehouses scale up/down based on query demand; remain active based on usage settings.Pre-warmed VMs (idle terminate)
    Resource AllocationConfigurable resources, manual start/stop, and autoscaling available.Dynamically allocated resources based on job requirements, with autoscaling.Autoscaling based on SQL query demand; optimized for SQL workloads.
    CostAlways running unless manually stopped or auto-terminated, can be expensive if left running.More cost-efficient for scheduled jobs, as the cluster runs only during the job execution.Efficient for SQL queries with autoscaling; cost based on query execution.Optimizes cluster creation
    PerformanceGood for interactive, collaborative workloads but may incur higher costs if not optimized.Highly performant for running isolated, parallel jobs without interference from other workloads.Optimized for low-latency SQL query performance and concurrent query execution.
    ScalingCan scale automatically based on workload demand (within limits set by the user).Scales based on the job’s needs; new clusters can be created for each job.Scales automatically to accommodate concurrent SQL queries.
    IsolationNot isolated — multiple users can share the cluster, which may impact performance.Fully isolated — each job runs on a separate cluster.Isolated SQL queries but shared resources for concurrent workloads.Shared resource pool
    Ideal ForData exploration, notebook development, machine learning experiments, ad-hoc queries.Scheduled ETL/ELT jobs, production jobs, or one-time data processing tasks.SQL analytics, dashboards, and BI tool integration for querying Delta Lake.Supporting clusters
    Supported LanguagesPython, Scala, R, SQL, and more via notebooks.Python, Scala, R, SQL (job-specific).SQL only.
    ManagementRequires manual monitoring and termination.Automatic termination after job completion.Automatically managed scaling and uptime based on usage.Faster cluster launches
    Example Use CaseRunning notebooks to explore and analyze data, performing machine learning experiments.Running a scheduled Spark job that processes data in a pipeline or transformation.Running SQL queries on Delta Lake, powering dashboards, or connecting to BI tools.
    Restart BehaviorCan be manually stopped and restarted; the Cluster ID remains the same.Automatically created and terminated for each job run; new Cluster ID for each job.SQL Warehouse remains active based on usage, auto-scaling handles load; Warehouse ID remains the same.Faster cluster launches
    Side by side clusters comparisons.

    Summary:

    • All-Purpose Clusters are ideal for interactive data exploration and multi-user environments, but they can be costly if left running for too long.
    • Job Clusters are used for single, isolated tasks (like scheduled ETL jobs) and are cost-effective since they are automatically created and terminated.
    • SQL Warehouses are specialized for SQL queries and business intelligence reporting, offering cost efficiency through on-demand scaling for SQL analytics.

    Please do not hesitate to contact me if you have any questions at William . chen @ mainri.ca

    (remove all space from the email account 😊)