Delta Lake

Author: m | 2025-04-24

Learn how to use Delta Lake. Welcome to the Delta Lake documentation. This is the documentation site for Delta Lake. dbdemos - Databricks Lakehouse demos : Delta Lake. Delta Lake Store your table with Delta Lake discover how Delta Lake can simplify your Data Pipelines.

Delta lake and the delta architecture

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

Why Use Delta Lake - Delta Lake Documentation

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

Data Lake, Lake House, and Delta Lake

Into memory are then resident in memory. Future queries that involve only resident columns don't need to load any more columns into memory.A column remains resident until there's reason for it to be removed (evicted) from memory. Reasons that columns might get removed include:The model or table was refreshed after a Delta table update at the source (see Framing in the next section).No query used the column for some time.Other memory management reasons, including memory pressure in the capacity due to other, concurrent operations.Your choice of Fabric SKU determines the maximum available memory for each Direct Lake semantic model on the capacity. For more information about resource guardrails and maximum memory limits, see Fabric capacity guardrails and limitations later in this article.FramingFraming provides model owners with point-in-time control over what data is loaded into the semantic model. Framing is a Direct Lake operation triggered by a refresh of a semantic model, and in most cases takes only a few seconds to complete. That's because it's a low-cost operation where the semantic model analyzes the metadata of the latest version of the Delta Lake tables and is updated to reference the latest Parquet files in OneLake.When framing occurs, resident table column segments and dictionaries might be evicted from memory if the underlying data has changed and the point in time of the refresh becomes the new baseline for all future transcoding events. From this point, Direct Lake queries only consider data in the Delta tables as of the time of the most recent framing operation. For that reason, Direct Lake tables are queried to return data based on the state of the Delta table at the point of the most recent framing operation. That time isn't necessarily the latest state of the Delta tables.Note that the semantic model analyzes the Delta log of each Delta table during framing to drop only the affected column segments and to reload newly added data during transcoding. An important optimization is that dictionaries will usually not be dropped when incremental framing takes effect, and new values are added to the existing dictionaries. This incremental framing approach helps to reduce the reload burden and benefits query performance. In the ideal case, when a Delta table received no updates, no reload is necessary for columns already resident in memory and queries show far less performance impact after framing because incremental framing essentially enables the semantic model to update substantial portions of the existing in-memory data in place.The following diagram shows how Direct Lake framing operations work.The diagram depicts the following processes and features.ItemDescriptionA semantic model exists in a Fabric workspace.Framing operations take place periodically, and they set the baseline for all future transcoding events. Framing operations can. Learn how to use Delta Lake. Welcome to the Delta Lake documentation. This is the documentation site for Delta Lake. dbdemos - Databricks Lakehouse demos : Delta Lake. Delta Lake Store your table with Delta Lake discover how Delta Lake can simplify your Data Pipelines.

Delta Lake 3.0.0

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 3.1.0

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

What is a Delta Lake?

Happen automatically, manually, on schedule, or programmatically.OneLake stores metadata and Parquet files, which are represented as Delta tables.The last framing operation includes Parquet files related to the Delta tables, and specifically the Parquet files that were added before the last framing operation.A later framing operation includes Parquet files added after the last framing operation.Resident columns in the Direct Lake semantic model might be evicted from memory, and the point in time of the refresh becomes the new baseline for all future transcoding events.Subsequent data modifications, represented by new Parquet files, aren't visible until the next framing operation occurs.It's not always desirable to have data representing the latest state of any Delta table when a transcoding operation takes place. Consider that framing can help you provide consistent query results in environments where data in Delta tables is transient. Data can be transient for several reasons, such as when long-running extract, transform, and load (ETL) processes occur.Refresh for a Direct Lake semantic model can be done manually, automatically, or programmatically. For more information, see Refresh Direct Lake semantic models.For more information about Delta table versioning and framing, see Understand storage for Direct Lake semantic models.Automatic updatesThere's a semantic model-level setting to automatically update Direct Lake tables. It's enabled by default. It ensures that data changes in OneLake are automatically reflected in the Direct Lake semantic model. You should disable automatic updates when you want to control data changes by framing, which was explained in the previous section. For more information, see Manage Direct Lake semantic models.TipYou can set up automatic page refresh in your Power BI reports. It's a feature that automatically refreshes a specific report page providing that the report connects to a Direct Lake semantic model (or other types of semantic model).DirectQuery fallbackA query sent to a Direct Lake semantic model can fall back to DirectQuery mode. In this case, it retrieves data directly from the SQL analytics endpoint of the lakehouse or warehouse. Such queries always return the latest data because they're not constrained to the point in time of the last framing operation.A query always falls back when the semantic model queries a view in the SQL analytics endpoint, or a table in the SQL analytics endpoint that enforces row-level security (RLS).Also, a query might fall back when the semantic model exceeds the guardrails of the capacity.ImportantIf possible, you should always design your solution—or size your capacity—to avoid DirectQuery fallback. That's because it might result in slower query performance.You can control fallback of your Direct Lake semantic models by setting its DirectLakeBehavior property. For more information, see Set the Direct Lake behavior property.Fabric capacity guardrails and limitationsDirect Lake semantic models require a Fabric capacity license. Also, there are

Delta Lake quickstartDelta Lake Documentation

Efficient data management and query optimization. Delta Lake also offers features like time travel, which allows users to access and revert to previous versions of data, and schema evolution, which enables schema updates without interrupting existing pipelines. This architecture enhances data reliability, data quality, and data governance, making it easier for organizations to maintain data integrity and consistency throughout the data lifecycle. Delta Lake architecture is well-suited for large-scale data engineering and analytics projects that require strong data consistency and reliability. Delta Lake is a game changer. Discover great training resource from the Databricks community at: or reach out to us at 3Cloud. Our expert team and solution offerings can help your business with any Azure product or service, including Managed Services offerings. By leveraging 3Cloud’s services and resources, organizations can enhance their understanding and capabilities around data lakes and Delta Lake technology, ensuring they are well-equipped to manage their data effectively in the cloud.. Learn how to use Delta Lake. Welcome to the Delta Lake documentation. This is the documentation site for Delta Lake.

Delta Lake QuickstartDelta Lake Documentation

Elements.Also, OneLake integration automatically writes data for tables in Import storage mode to Delta tables in OneLake without involving any migration effort. By using this option, you can realize many of the benefits of Fabric that are made available to Import semantic model users, such as integration with lakehouses through shortcuts, SQL queries, notebooks, and more. We recommend that you consider this option as a quick way to reap the benefits of Fabric without necessarily or immediately re-designing your existing data warehouse and/or analytics system.Direct Lake storage mode is also suitable for minimizing data latency to quickly make data available to business users. If your Delta tables are modified intermittently (and assuming you already did data preparation in the data lake), you can depend on automatic updates to reframe in response to those modifications. In this case, queries sent to the semantic model return the latest data. This capability works well in partnership with the automatic page refresh feature of Power BI reports.Keep in mind that Direct Lake depends on data preparation being done in the data lake. Data preparation can be done by using various tools, such as Spark jobs for Fabric lakehouses, T-SQL DML statements for Fabric warehouses, dataflows, pipelines, and others. This approach helps ensure data preparation logic is performed as low as possible in the architecture to maximize reusability. However, if the semantic model author doesn't have the ability to modify the source item, for example, if a self-service analyst doesn't have write permissions on a lakehouse that is managed by IT, then Import storage mode might be a better choice. That's because it supports data preparation by using Power Query, which is defined as part of semantic model.Be sure to factor in your current Fabric capacity license and the Fabric capacity guardrails when you consider Direct Lake storage mode. Also, factor in the considerations and limitations, which are described later in this article.TipWe recommend that you produce a prototype—or proof of concept (POC)—to determine whether a Direct Lake semantic model is the right solution, and to mitigate risk.How Direct Lake worksTypically, queries sent to a Direct Lake semantic model are handled from an in-memory cache of the columns sourced from Delta tables. The underlying storage for a Delta table is one or more Parquet files in OneLake. Parquet files organize data by columns rather than rows. Semantic models load entire columns from Delta tables into memory as they're required by queries.A Direct Lake semantic model might also use DirectQuery fallback, which involves seamlessly switching to DirectQuery mode. DirectQuery fallback retrieves data directly from the SQL analytics endpoint of the lakehouse or the warehouse. For example, fallback might occur when a Delta table contains more rows

2. Installing Delta Lake - Delta Lake: The Definitive Guide [Book]

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Delta Lake with Python: How to Use Delta Lake Without Spark

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