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.

Creating and Using Delta Tables in PySpark or SQL

create a Delta table by writing a DataFrame in PySpark or SQL.

Create or Write a DataFrame to a Delta table

If we directly query delta table from adls using SQL, always use

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

# python
# Write a DataFrame to a Delta table
df.write.format("delta").save("/mnt/delta/my_delta_table")


# sql
-- Creating a Delta Table
CREATE TABLE my_delta_table
USING delta
LOCATION '/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.


# 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); 

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

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Data lake vs delta lake vs data lakehouse, and data warehouses comparison

As a data engineer, we often hear terms like Data Lake, Delta Lake, Data Lakehouse, and data warehouse, which might be confusing at times. Today, we’ll explain these terms and talk about the differences of each of the technologies and concepts, along with scenarios of usage for each.

Delta Lake

Delta lake is an open-source technology, we don’t have a Delta Lake; you use Delta Lake to store your 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.

Delta Lake takes your existing Parquet data lake and makes it more reliable and performant by:

  1. Storing all the metadata in a separate transaction log
  2. Tracking all the changes to your data in this transaction log
  3. Organizing your data for maximum query performance

Data Lakehouse

Data lakehouse is a new, open data management architecture that combines the flexibility, cost-efficiency, and scale of data lakes with the data management and ACID transactions of data warehouses, enabling business intelligence (BI) and machine learning (ML) on all data.

Data Lake

A data lake is a centralized repository that allows organizations to store vast amounts of structured, semi-structured, and unstructured data. Unlike traditional data warehouses, a data lake retains data in its raw form until it is needed, which provides flexibility in how the data can be used.

Data Warehouse

A data warehouse is a centralized repository that stores structured data (database tables, Excel sheets) and semi-structured data (XML files, webpages) Its data is usually cleaned and standardized for the purposes of reporting and analysis. 

Data lakes vs. data lakehouse vs. data warehouses

follow table simply compared what difference .

 Data lakeData lakehouseData warehouse
Types of dataAll types: Structured data, semi-structured data, unstructured (raw) dataAll types: Structured data, semi-structured data, unstructured (raw) dataStructured data only
Cost$$$$$
FormatOpen formatOpen formatClosed, proprietary format
ScalabilityScales to hold any amount of data at low cost, regardless of typeScales to hold any amount of data at low cost, regardless of typeScaling up becomes exponentially more expensive due to vendor costs
Intended usersLimited: Data scientistsUnified: Data analysts, data scientists, machine learning engineersLimited: Data analysts
ReliabilityLow quality, data swampHigh quality, reliable dataHigh quality, reliable data
Ease of useDifficult: Exploring large amounts of raw data can be difficult without tools to organize and catalog the dataSimple: Provides simplicity and structure of a data warehouse with the broader use cases of a data lakeSimple: Structure of a data warehouse enables users to quickly and easily access data for reporting and analytics
PerformancePoorHighHigh

summary

Data lakes are a good technology that give you flexible and low-cost data storage. Data lakes can be a great choice for you if:

  • You have data in multiple formats coming from multiple sources
  • You want to use this data in many different downstream tasks, e.g. analytics, data science, machine learning, etc.
  • You want flexibility to run many different kinds of queries on your data and do not want to define the questions you want to ask your data in advance
  • You don’t want to be locked into a vendor-specific proprietary table format

Data lakes can also get messy because they do not provide reliability guarantees. Data lakes are also not always optimized to give you the fastest query performance.

Delta Lake is almost always more reliable, faster and more developer-friendly than a regular data lake. Delta lake can be a great choice for you because:

  • You have data in multiple formats coming from multiple sources
  • You want to use this data in many different downstream tasks, e.g. analytics, data science, machine learning, etc.
  • You want flexibility to run many different kinds of queries on your data and do not want to define the questions you want to ask your data in advance
  • You don’t want to be locked into a vendor-specific proprietary table format

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

(remove all space from the email account 😊)