A Comprehensive Guide to Building an Open Data Lakehouse

Building an open data lakehouse is a complex task, but with the right approach, it can be a game-changer for your organization.

A data lakehouse is a centralized repository that stores both structured and unstructured data, allowing for flexible and scalable data management. It's designed to handle large volumes of data from various sources, including social media, IoT devices, and applications.

To get started, you'll need to define your data lakehouse architecture, which typically includes a data ingestion layer, a data storage layer, and a data serving layer. The data ingestion layer is responsible for collecting and processing data from various sources, while the data storage layer stores the data in a scalable and secure manner.

The data serving layer provides a unified interface for querying and analyzing the data, making it easier to gain insights and make informed decisions.

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A data lakehouse is a new, open data management architecture that combines the flexibility and cost-efficiency of data lakes with the data management and ACID transactions of data warehouses.

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This architecture enables business intelligence and machine learning on all data, which is a game-changer for organizations looking to unlock the full potential of their data.

Data lakehouses provide a unified platform for analytics, allowing businesses to power all their analytics on one platform.

By combining the benefits of data lakes and data warehouses, lakehouses offer a more efficient and effective way to manage and analyze data.

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An open data lakehouse architecture can bring numerous benefits to your organization, including the ability to combine the benefits of data warehouses and data lakes into a single, performant analytics platform.

This unified platform enables data teams to move faster and make more informed decisions by providing access to the most complete and up-to-date data available.

Some of the key benefits of implementing an open data lakehouse architecture include:

By leveraging metadata layers, such as Delta Lake, data lakehouses can offer rich management features like ACID-compliant transactions, schema enforcement, and data validation.

Data lakehouses also provide optimized access for data science and machine learning tools, making it easy for data scientists and machine learning engineers to access the data in the lakehouse.

The open data formats used by data lakehouses, like Parquet, make it easy for data scientists and machine learning engineers to access the data in the lakehouse.

Data lakehouses also ensure that teams have the most complete and up-to-date data available for data science, machine learning, and business analytics projects.

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An open data lakehouse platform builds upon commercial cloud object storage services and draws from the open-source ecosystem to construct a scalable, performant analytics solution.

The three open-source components of an open data lakehouse platform are file formats, table formats, and compute engines.

File formats like Parquet and ORC files store table data in HDFS or an S3 data lake. Table formats like Delta Lake, Apache Iceberg, and Apache Hudi provide metadata layers for data management.

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These metadata layers store metadata in the same data lake as JSON or Avro format and have a catalog pointer to the current metadata.

An analytics engine that supports the data lakehouse spec is also a key component, with popular choices including Apache Spark, Trino, and Dremio.

Here are some key components in a data lakehouse implementation:

Metadata layers like Delta Lake sit on top of open file formats and track which files are part of different table versions to offer rich management features like ACID-compliant transactions.

Apache Iceberg is the key building block of the open lakehouse, offering high-performance capabilities like time travel and snapshot isolation.

Commodity object storage, provided by cloud platforms like Amazon S3 and Azure Blob Storage, decouples storage from compute, allowing data teams to optimize costs and performance independently.

Open file formats, such as Apache Parquet and ORC, structure data in columnar ways that enhance query performance by providing detailed metadata and indexes.

Trino is an ideal solution for performing big data analytics on object storage, thanks to its parallelization and query optimizations that reduce compute costs and leverage source indexes.

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Commodity object storage is a game-changer for data teams.

Amazon S3, Azure Blob Storage, and other cloud platforms provide commodity data storage at petabyte scales.

These cloud platforms are more flexible, scalable, and affordable than on-premises storage infrastructure.

Decoupling storage from compute allows data teams to optimize costs and performance independently.

Cloud platforms like Amazon S3 and Azure Blob Storage can handle massive amounts of data with ease.

By leveraging commodity object storage, data teams can reduce costs and improve efficiency.

With commodity object storage, data teams can focus on analyzing data rather than managing storage infrastructure.

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Apache Iceberg is a high-performance open table format for large analytic tables that brings the reliability of SQL tables to big data. It's a key building block of the open lakehouse.

Apache Iceberg makes it possible for multiple compute engines to work concurrently, which is a big deal for big data analytics. It offers rich capabilities like time travel, snapshot isolation, schema evolution, hidden partitioning, and more.

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Open table formats like Apache Iceberg add an abstraction layer to a data lake's sparse metadata, creating warehouse-like storage with structured and unstructured data. They define the schema and partitions of every table and describe the files they contain.

Apache Iceberg and Delta Lake are commonly used open table formats that provide a separate reference for metadata information. This helps queries avoid opening every file's header and instead go to the most relevant files.

Using an open table format like Apache Iceberg can better meet modern analytics needs than older technologies like Hive. Some benefits include improved performance, reliability, and scalability.

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Secure and governed data is essential for any open data lakehouse. The Iceberg tables in Cloudera integrate within SDX, allowing for unified security, fine-grained policies, governance, lineage, and metadata management across multiple clouds.

To manage data governance, you need to ensure that open data is compliant with legal and ethical regulations, is secure, and is of high quality. This includes establishing data access controls, data classification, and data retention policies.

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Metadata management is crucial for open data lakehouses, as it helps describe the characteristics and properties of data, including where it came from, who owns it, and how it can be used. Delta Lake is a popular choice for metadata management, and it's easy to get started with.

To implement Delta Lake, you need to add four jars to your Spark environment: delta-core, delta-storage, antlr4-runtime, and jackson-core-asl. You can download these jars from the Maven repo and add them to your Spark container image.

Here are the key configurations you need to add to your Spark session for Delta Lake integration:

By implementing these best practices for management and governance, you can ensure that your open data lakehouse is secure, compliant, and properly documented, making it easier to discover and utilize your data.