Blog: What is a Data Lakehouse and a Table Format?


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Iceberg Lakehouse Engineering Video Playlist The value of the lakehouse model, along with the concept of "shifting left" by moving more data modeling and processing from the data warehouse to the data lake, has seen significant buy-in and adoption over the past few years. A lakehouse integrates data warehouse functionality into a data lake using open table formats, offering the best of both worlds for analytics and storage. Enabling lakehouse architecture with open table formats like Apache Iceberg, Delta Lake, Apache Hudi, and Apache Paimon has introduced the need to manage interoperability between these formats, especially at the boundaries of data systems. While many lakehouse implementations operate seamlessly with a single table format, scenarios arise where multiple formats are involved. To address these challenges, several solutions have emerged. In this blog, we will explore these solutions and discuss when it makes sense to use them. The Solutions There are primarily two types of interoperability solutions for working across different table formats: 1. Mirroring Metadata These solutions focus on maintaining metadata for the same data files in multiple formats, enabling seamless interaction across systems. Apache XTable: An open-source project initially developed at Onehouse and now managed by the community, Apache XTable enables bi-directional metadata conversion between different table formats. It includes incremental metadata update features, ensuring efficiency and consistency. For Iceberg, XTable generates the metadata, which can then be registered with your preferred catalog. Delta Lake Uniform: A feature of the Delta Lake format, Delta Lake Uniform allows you to natively write to Delta Lake tables while maintaining a secondary metadata set in Iceberg or Hudi. For Iceberg, it can sync these tables to a Hive Metastore or Unity Catalog. When used with Unity Catalog, these tables can also be exposed for reading through an Iceberg REST Catalog interface, enabling greater flexibility and integration. 2. Data Unification Platforms Unified Lakehouse Platforms like Dremio or open-source query engines such as Trino provide another solution by allowing queries across multiple formats without requiring metadata conversion. This approach enables various table formats to coexist while being queried seamlessly. Dremio’s Advantage with Apache Arrow and Reflections: Dremio leverages the power of Apache Arrow to enable in-memory columnar processing, delivering greater performance to Trino. Additionally, Dremio’s Reflections feature provides pre-aggregated, incremental materializations that significantly accelerate query response times especially when paired with Apache Iceberg tables. With its built-in semantic layer, Dremio ensures uniform data models that can be consistently utilized across different teams and tools. This capability enables seamless collaboration, allowing data engineers, analysts, and BI tools to consume data efficiently without requiring duplicate efforts for model creation or maintenance. The Use Cases and Which Solution to Use 1. Joining Delta Lake Tables with On-Prem Data If you're a Databricks user leveraging the Databricks ecosystem and its features but also have on-premises data you'd like to incorporate into certain workflows, a hybrid tool like Dremio can help. Dremio enables you to read Delta Lake tables directly from cloud storage and federate queries with your on-prem data. However, this approach bypasses the governance settings in Unity Catalog and doesn’t take full advantage of Dremio's powerful acceleration features, such as Live Reflections and Incremental Reflections. A better option is to connect Dremio to Unity Catalog tables and read the Uniform Iceberg version of the metadata. This allows you to maintain Unity Catalog governance while also leveraging Dremio’s advanced acceleration capabilities for optimized query performance. 2. Streaming With Hudi and Reading as Iceberg/Delta Apache Hudi is widely used for low-latency, high-frequency upserts in streaming use cases. However, when it comes to consuming this data, broader read support exists for Iceberg and Delta Lake. This is an ideal scenario for Apache XTable, which can handle a continuous, one-way incremental metadata conversion. As data lands in Hudi, XTable can write new metadata in the preferred format, such as Iceberg or Delta, ensuring seamless consumption. 3. Using Snowflake and Databricks Side by Side Snowflake in 2024 announce Polaris which has since become a community-run Incubating Apache project. Snowflake offers a managed Polaris service called Open Catalog. Apache Polaris features the ability to connect "external catalogs." This functionality allows Snowflake to read tables from other Iceberg REST Catalog-compliant systems, such as Nessie, Gravitino, Lake Keeper, AWS Glue, and Unity Catalog directly from Polaris. By connecting Unity Catalog as an external catalog, you can utilize Uniform-enabled tables from Delta Lake alongside other datasets within Snowflake, enabling seamless interoperability between Snowflake and Databricks environments. 4. Migrating Between Formats If you're looking to migrate between table formats without rewriting all your data, Apache XTable stands out as the optimal solution. XTable enables smooth transitions allowing you to adopt a new format with minimal disruption to your existing workflows. Limitations to Keep in Mind When using a mirrored metadata approach to interoperability, there are certain trade-offs to be aware of. One key limitation is the loss of write-side optimizations specific to the secondary format, such as hidden partitioning in Iceberg or deletion vectors in Delta Lake. Below is a list of specific limitations when using Uniform or XTable: Uniform-enabled Delta Lake tables do not currently support Liquid Clustering.
Deletion Vectors cannot be utilized with Uniform-enabled Delta Lake tables.
XTable supports only Copy-on-Write or Read-Optimized Views of tables.
XTable has limited support for Delta Lake's Generated Columns. Conclusion As organizations increasingly adopt lakehouse architectures, interoperability across multiple table formats has become a critical need. Solutions like Apache XTable and Delta Lake Uniform offer powerful ways to manage metadata and facilitate collaboration between different systems. Whether you're joining Delta Lake tables with on-premises data, leveraging Hudi for streaming, integrating Snowflake with Databricks, or migrating between formats, these tools provide flexibility and efficiency. However, it’s important to evaluate the limitations of each approach to ensure it aligns with your use case. While mirrored metadata solutions simplify interoperability, they come with trade-offs, particularly on the write-side optimizations of the secondary format. By understanding these constraints and leveraging platforms like Dremio for advanced query acceleration and data unification, you can make informed decisions and maximize the potential of your lakehouse ecosystem. Blog: What is a Data Lakehouse and a Table Format?
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Iceberg Lakehouse Engineering Video Playlist Blog: What is a Data Lakehouse and a Table Format? Free Copy of Apache Iceberg the Definitive Guide Free Apache Iceberg Crash Course Lakehouse Catalog Course Iceberg Lakehouse Engineering Video Playlist Blog: What is a Data Lakehouse and a Table Format? Blog: What is a Data Lakehouse and a Table Format? Blog: What is a Data Lakehouse and a Table Format? Free Copy of Apache Iceberg the Definitive Guide Free Copy of Apache Iceberg the Definitive Guide Free Copy of Apache Iceberg the Definitive Guide Free Apache Iceberg Crash Course Free Apache Iceberg Crash Course Free Apache Iceberg Crash Course Lakehouse Catalog Course Lakehouse Catalog Course Lakehouse Catalog Course Iceberg Lakehouse Engineering Video Playlist Iceberg Lakehouse Engineering Video Playlist Iceberg Lakehouse Engineering Video Playlist The value of the lakehouse model , along with the concept of "shifting left" by moving more data modeling and processing from the data warehouse to the data lake, has seen significant buy-in and adoption over the past few years. A lakehouse integrates data warehouse functionality into a data lake using open table formats, offering the best of both worlds for analytics and storage. lakehouse model Enabling lakehouse architecture with open table formats like Apache Iceberg, Delta Lake, Apache Hudi, and Apache Paimon has introduced the need to manage interoperability between these formats, especially at the boundaries of data systems. While many lakehouse implementations operate seamlessly with a single table format, scenarios arise where multiple formats are involved. To address these challenges, several solutions have emerged. In this blog, we will explore these solutions and discuss when it makes sense to use them. The Solutions There are primarily two types of interoperability solutions for working across different table formats: 1. Mirroring Metadata These solutions focus on maintaining metadata for the same data files in multiple formats, enabling seamless interaction across systems. Apache XTable: An open-source project initially developed at Onehouse and now managed by the community, Apache XTable enables bi-directional metadata conversion between different table formats. It includes incremental metadata update features, ensuring efficiency and consistency. For Iceberg, XTable generates the metadata, which can then be registered with your preferred catalog. Delta Lake Uniform: A feature of the Delta Lake format, Delta Lake Uniform allows you to natively write to Delta Lake tables while maintaining a secondary metadata set in Iceberg or Hudi. For Iceberg, it can sync these tables to a Hive Metastore or Unity Catalog. When used with Unity Catalog, these tables can also be exposed for reading through an Iceberg REST Catalog interface, enabling greater flexibility and integration. 2. Data Unification Platforms Unified Lakehouse Platforms like Dremio or open-source query engines such as Trino provide another solution by allowing queries across multiple formats without requiring metadata conversion. This approach enables various table formats to coexist while being queried seamlessly. Dremio Trino Dremio’s Advantage with Apache Arrow and Reflections: Dremio leverages the power of Apache Arrow to enable in-memory columnar processing, delivering greater performance to Trino. Additionally, Dremio’s Reflections feature provides pre-aggregated, incremental materializations that significantly accelerate query response times especially when paired with Apache Iceberg tables. Reflections With its built-in semantic layer, Dremio ensures uniform data models that can be consistently utilized across different teams and tools. This capability enables seamless collaboration, allowing data engineers, analysts, and BI tools to consume data efficiently without requiring duplicate efforts for model creation or maintenance. The Use Cases and Which Solution to Use 1. Joining Delta Lake Tables with On-Prem Data If you're a Databricks user leveraging the Databricks ecosystem and its features but also have on-premises data you'd like to incorporate into certain workflows, a hybrid tool like Dremio can help. Dremio enables you to read Delta Lake tables directly from cloud storage and federate queries with your on-prem data. Dremio However, this approach bypasses the governance settings in Unity Catalog and doesn’t take full advantage of Dremio's powerful acceleration features, such as Live Reflections and Incremental Reflections . Live Reflections Incremental Reflections A better option is to connect Dremio to Unity Catalog tables and read the Uniform Iceberg version of the metadata. This allows you to maintain Unity Catalog governance while also leveraging Dremio’s advanced acceleration capabilities for optimized query performance. 2. Streaming With Hudi and Reading as Iceberg/Delta Apache Hudi is widely used for low-latency, high-frequency upserts in streaming use cases. However, when it comes to consuming this data, broader read support exists for Iceberg and Delta Lake. This is an ideal scenario for Apache XTable , which can handle a continuous, one-way incremental metadata conversion. As data lands in Hudi, XTable can write new metadata in the preferred format, such as Iceberg or Delta, ensuring seamless consumption. Apache XTable 3. Using Snowflake and Databricks Side by Side Snowflake in 2024 announce Polaris which has since become a community-run Incubating Apache project. Snowflake offers a managed Polaris service called Open Catalog . Apache Polaris features the ability to connect "external catalogs." This functionality allows Snowflake to read tables from other Iceberg REST Catalog-compliant systems, such as Nessie , Gravitino , Lake Keeper , AWS Glue , and Unity Catalog directly from Polaris. Open Catalog Nessie Gravitino Lake Keeper AWS Glue Unity Catalog By connecting Unity Catalog as an external catalog, you can utilize Uniform-enabled tables from Delta Lake alongside other datasets within Snowflake, enabling seamless interoperability between Snowflake and Databricks environments. Uniform-enabled tables 4. Migrating Between Formats If you're looking to migrate between table formats without rewriting all your data, Apache XTable stands out as the optimal solution. XTable enables smooth transitions allowing you to adopt a new format with minimal disruption to your existing workflows. Apache XTable Limitations to Keep in Mind When using a mirrored metadata approach to interoperability, there are certain trade-offs to be aware of. One key limitation is the loss of write-side optimizations specific to the secondary format, such as hidden partitioning in Iceberg or deletion vectors in Delta Lake. Below is a list of specific limitations when using Uniform or XTable: hidden partitioning deletion vectors Uniform-enabled Delta Lake tables do not currently support Liquid Clustering. Deletion Vectors cannot be utilized with Uniform-enabled Delta Lake tables. XTable supports only Copy-on-Write or Read-Optimized Views of tables. XTable has limited support for Delta Lake's Generated Columns. Uniform-enabled Delta Lake tables do not currently support Liquid Clustering . Uniform-enabled Delta Lake tables Liquid Clustering Deletion Vectors cannot be utilized with Uniform-enabled Delta Lake tables. Deletion Vectors XTable supports only Copy-on-Write or Read-Optimized Views of tables. XTable Copy-on-Write Read-Optimized Views XTable has limited support for Delta Lake's Generated Columns . limited support Generated Columns Conclusion As organizations increasingly adopt lakehouse architectures, interoperability across multiple table formats has become a critical need. Solutions like Apache XTable and Delta Lake Uniform offer powerful ways to manage metadata and facilitate collaboration between different systems. Apache XTable Delta Lake Uniform Whether you're joining Delta Lake tables with on-premises data, leveraging Hudi for streaming, integrating Snowflake with Databricks, or migrating between formats, these tools provide flexibility and efficiency. However, it’s important to evaluate the limitations of each approach to ensure it aligns with your use case. While mirrored metadata solutions simplify interoperability, they come with trade-offs, particularly on the write-side optimizations of the secondary format. By understanding these constraints and leveraging platforms like Dremio for advanced query acceleration and data unification, you can make informed decisions and maximize the potential of your lakehouse ecosystem. Dremio Blog: What is a Data Lakehouse and a Table Format? Free Copy of Apache Iceberg the Definitive Guide Free Apache Iceberg Crash Course Lakehouse Catalog Course Iceberg Lakehouse Engineering Video Playlist Blog: What is a Data Lakehouse and a Table Format? Blog: What is a Data Lakehouse and a Table Format? Free Copy of Apache Iceberg the Definitive Guide Free Copy of Apache Iceberg the Definitive Guide Free Apache Iceberg Crash Course Free Apache Iceberg Crash Course Lakehouse Catalog Course Lakehouse Catalog Course Iceberg Lakehouse Engineering Video Playlist Iceberg Lakehouse Engineering Video Playlist

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