Delta Lake

The building of a data lakehouse. Common lakehouses include the Databricks Lakehouse and Azure Databricks. Delta Lakes deliver an open-source storage layer that brings ACID transactions to Apache Spark big data workloads. So, instead of facing the challenges described above, you have an over layer of your data lake from Delta Lake. Delta Lake provides ACID transactions through a log that is associated with each Delta table created in your data lake. This log records the history of everything that was ever done to that data table or data set, therefore you gain high levels of reliability and stability to your data lake. Key Features Defining Delta Lake ACID Transactions (Atomicity, Consistency, Isolation, Durability) – With Delta you don’t need to write any code – it’s automatic that transactions are written to the log. This transaction log is the key, and it represents a single source of truth. This means that data operations within Delta Lake, such as inserts, updates, and deletes, are atomic and isolated, guaranteeing consistent and reliable results. Scalable Metadata Handling – Handles terabytes or even petabytes of data with ease. Metadata is stored just like data and you can display it using a feature of the syntax called Describe Detail which will describe the detail of all the metadata that is associated with the table. Puts the full force of Spark against your metadata. Unified Batch & Streaming – No longer a need to have separate architectures for reading a stream of data versus a batch of data, so it overcomes limitations of streaming and batch systems. Delta Lake Table is a batch and streaming source and sink. You can do concurrent streaming or batch writes to your table and it all gets logged, so it’s safe and sound in your Delta table. Schema Enforcement – this is what makes Delta strong in this space as it enforces your schemas. If you put a schema on a Delta table and you try to write data to that table that is not conformant with the schema, it will give you an error and not allow you to

Write that, preventing you from bad writes. The enforcement methodology reads the schema as part of the metadata; it looks at every column, data type, etc. and ensures what you’re writing to the Delta table is the same as what the schema represents of your Delta table – no need to worry about writing bad data to your table. Delta Lake supports schema evolution, allowing users to evolve the schema of their data over time without interrupting existing pipelines or breaking downstream applications. This flexibility simplifies the process of incorporating changes and updates to data structures. Time Travel (Data Versioning) – you can query an older snapshot of your data, provide data versioning, and roll back or audit data. Delta Lake allows users to access and analyze previous versions of data through time travel capabilities. This feature enables data exploration and analysis at different points in time, making it easier to track changes, identify trends, and perform historical analysis. Upserts and Deletes – these operations are typically hard to do without something like Delta. Delta allows you to do upserts or merges very easily. Merges are like SQL merges into your Delta table and you can merge data from another data frame into your table and do updates, inserts, and deletes. You can also do a regular update or delete of data with a predicate on a table – something that was almost unheard of before Delta. 100% Compatible with Apache Spark Optimized File Management: Delta Lake organizes data into optimized Parquet files and maintains metadata to enable efficient file management. It leverages file-level operations like compaction, partitioning, and indexing to optimize query performance and reduce storage costs. Delta Lake Architecture Delta Lake architecture is an advanced and reliable data storage and processing framework built on top of a data lake. It extends the capabilities of traditional data lakes by providing ACID (Atomicity, Consistency, Isolation, Durability) transactional properties, schema enforcement, and data versioning. In Delta Lake, data is organized into a set of Parquet files, which are stored in a distributed file system. It maintains metadata about these files, enabling

Hi @Vinod Kumar Kapa Welcome to Microsoft Q&A platform and thanks for posting your question here. According to the Azure documentation, querying Delta Lake format in serverless Synapse SQL pool is currently in public preview. This preview version is provided without a service level agreement, and it's not recommended for production workloads. Certain features might not be supported or might have constrained capabilities. Therefore, it is possible to encounter significant scanning overhead and multiple entries in SQL requests when using the delta format for queries within a serverless SQL pool.To reduce the data scan, you can follow the best practices for serverless SQL pool provided by Azure. Here are some possible solutions: The data types you use in your query affect performance and concurrency. Use the smallest data size that can accommodate the largest possible value. If possible, use varchar and char instead of nvarchar and nchar. Use PARSER_VERSION 2.0 to query Delta Lake files: You can use a performance-optimized parser when you query Delta Lake files. Creating statistics for columns used in queries can improve query performance in Azure Synapse Analytics. The serverless SQL pool uses statistics to generate optimal query execution plans. While statistics are automatically created for some file types, they are not automatically created for Delta Lake files when using external tables. It's important to manually create statistics for Delta Lake files, especially for columns used in DISTINCT, JOIN, WHERE, ORDER BY, and GROUP BY clauses. Optimizing the partition strategy in your data lake can improve

Delta Lake is a technology that was developed by the same developers as Apache Spark. Delta Lake is an open-source storage layer created to run on top of an existing data lake and improve its reliability, security, and performance. It’s designed to bring reliability to your data lakes and provide Atomicity, Consistency, Isolation, and Durability (ACID) transactions, scalable metadata handling and unifies streaming and batch data processing. Delta Lake is integrated into the Databricks platform, providing a seamless experience for users to work with big data. Its compatibility with Apache Spark allows users to run their existing Spark jobs on Delta Lake with minimal changes, leveraging Spark’s powerful analytics capabilities on a more reliable and robust data storage foundation.What are Some Challenges of Data Lakes? Some challenges with data lakes include data indexing and partitioning, deleted files, unnecessary reads from disks and more. Data lakes are notoriously messy as everything gets dumped there. Sometimes, we may not have a rhyme or reason for dumping data there; we may be thinking we’ll need it at some later date. Data lakes, while powerful for storing vast amounts of unstructured and structured data, face two significant challenges. First, they often suffer from a lack of organization and governance, leading to what is known as a “data swamp” where data becomes inaccessible, unusable, and difficult to find due to poor management and metadata absence. Ensuring data quality and consistency is challenging because data lakes typically accept data in its original form without strict validation, leading to potential issues with accuracy, duplication, and incompleteness in the stored data.Much of this mess is because your data lake will have a lot of small files and different data types. Because there are many small files that are not compacted, trying to read them in any shape or form is difficult, if not impossible. Data lakes often contain bad data or corrupted data files so you can’t analyze them unless you go back and pretty much start over again. How To Overcome Data Lake Challenges This is where Delta Lake comes to the rescue! A Delta Lake enables