Data Lakehouse: A Unified Platform for Data Management

A data lakehouse is a game-changer for data management, offering a unified platform that combines the best of both worlds: the flexibility of a data lake and the structure of a data warehouse.

It provides a centralized location for storing and managing data, making it easier to access and analyze.

This approach eliminates the need for separate data lakes and warehouses, reducing costs and complexity.

By integrating data from various sources, a data lakehouse enables data teams to work more efficiently and effectively.

A data lakehouse is a hybrid solution that combines the benefits of both data lakes and data warehouses. It's designed to store and process large amounts of data in its raw form, similar to a data lake, but also provides data management and governance features like a data warehouse.

Delta Lake is an example of a technology that enables building a lakehouse architecture on top of cloud data lakes, addressing performance and consistency issues associated with data lakes.

Data lakehouses differ from data warehouses and data lakes in that all data can be ingested and stored in one platform and fully managed there. This allows for immediate processing and optimization of data for different kinds of queries and analytics uses.

With a data lakehouse, data sets can be processed and optimized for various queries and analytics uses immediately after data is ingested, making it ideal for BI and analytics applications that end users run regularly or on the fly as needed.

Data lakehouses provide versatility and scalability, offering the ability to store large amounts of data while also processing it in real time, making them an ideal hybrid solution for organizations that require both structured and unstructured data.

A data lakehouse offers numerous advantages, including scalability, improved data management, and lower costs. By storing diverse data, data lakehouses can support all data use cases, ranging from reporting to predictive modeling to generative AI.

One of the key features of a data lakehouse is its ability to support ACID transactions, which ensure consistency as multiple parties concurrently read and write data. This is achieved through a transactional metadata layer that sits on top of the underlying data lake.

Data lakehouses also support schema-on-read, where the software accessing the data determines its structure on the fly, and enforce schema adherence to minimize write job failures and ensure data reliability. This is made possible by the use of open storage formats like Parquet and Apache Avro.

Here are some of the key features of a data lakehouse:

Data lakes originated with Hadoop clusters in the early 2000s. They provide a lower-cost storage tier for a combination of structured, unstructured and semistructured data by using a file system or cloud object storage.

Data warehouses were first developed in the 1980s as a repository for structured data to support business intelligence and basic analytics. They're not suited to storing unstructured and semistructured data.

Data lakes can store varied sets of big data, but they often suffer issues with performance, data quality and inconsistencies. This is due to the way the data in them is managed and modified for different analytics uses.

Data lakes use a file system or cloud object storage instead of relational databases and disk storage common in data warehouses. This makes them a lower-cost option for storing big data.

A data lakehouse is a game-changer for organizations looking to simplify their analytics architecture. By providing a single storage and processing tier for all data, it streamlines the data engineering process and makes it easier to build data pipelines.

Data lakehouses offer several key advantages, including scalability, improved data management, and lower costs. They can handle diverse data types and workloads, and support both structured and unstructured data.

One of the biggest benefits of a data lakehouse is its ability to reduce data staleness. With a data lakehouse, data is often available for all types of analytics in a few hours, compared to the multiple days it sometimes takes to cleanse and transform new data and transfer it into a data warehouse.

Here are some of the key features and advantages of a data lakehouse:

Data lakehouses also address four problems with the two-tier architecture that spans separate data lake and data warehouse environments: reliability issues, data staleness, limits on advanced analytics, and high costs.

By providing a single platform that can handle all types of data and workloads, data lakehouses can simplify the overall analytics architecture and make it easier to build data pipelines.

Implementing a data lakehouse requires a modular and open design, allowing for the selection of best-of-breed engines and tools according to specific requirements.

The complexity of workloads and security considerations can vary greatly, making large-scale implementations require tailored approaches.

A data lakehouse architecture typically consists of five key layers.

To deploy a lakehouse, integrating Iceberg into the Shared Data Experience (SDX) offers the easiest path, with additional capabilities simplifying data management for large data sets.

These capabilities include schema evolution, hidden partition, and more, making it easier to manage complex data sets.

A data lakehouse can be implemented using a modular and open design, allowing for the selection of best-of-breed engines and tools according to specific requirements. This approach enables organizations to tailor their implementation to meet their unique needs.

Some key technologies and solutions that support data lakehouse architecture include cloud object storage, transactional metadata layers, and data optimization capabilities. These features enable efficient storage and management of diverse data types and workloads.

Here are some popular cloud object storage services used in data lakehouses:

These services provide scalable and cost-effective storage for large amounts of data, making them ideal for data lakehouse implementations.

A platform is essentially a foundation that supports various technologies and solutions. It's like a building block that enables different components to work together seamlessly.

Data lakehouses are a type of platform that combines the best of both worlds – data lakes and data warehouses. This modular and open design allows for the selection of best-of-breed engines and tools according to specific requirements.

A data lakehouse can store a wide range of data from both internal and external sources, making it available to various end users. This includes data scientists, data analysts, and business analysts, among others.

The key features of a data lakehouse include ACID transactions support, BI support, open storage formats, schema and governance capabilities, support for diverse data types and workloads, and decoupled storage and compute. These features offer data teams the following benefits and advantages: scalability, improved data management, streamlined data architecture, lower costs, and the elimination of redundant data.

Here are some of the end users that can access and utilize data stored in a data lakehouse:

Multi-Cloud is a game-changer for data management. It allows you to build a data lakehouse anywhere, on any public cloud or in your own data center.

You can build once and run anywhere without any headaches, thanks to Cloudera's full portability on all clouds. This means you can deploy your data services without worrying about compatibility issues.

A data lakehouse is a game-changer for organizations, and there are several key technologies and solutions that make it possible. One of the most popular open-source table formats is Delta Lake, which enables building a lakehouse architecture on top of cloud data lakes. Delta Lake offers an ACID-compliant layer that operates over cloud object stores, addressing performance and consistency issues associated with data lakes.

Delta Lake provides features like schema enforcement and evolution, time travel, efficient metadata handling, and DML operations, making it a robust data lakehouse platform. Another key technology is Apache Hudi, an open-source transactional data lakehouse platform built around a database kernel. Hudi provides table-level abstractions over open file formats like Apache Parquet and ORC, delivering core warehouse and database functionalities directly in the data lake.

Hudi also incorporates critical table services tightly integrated with its database kernel, managing aspects like table bookkeeping, metadata, and storage layouts across both ingested and derived data. This elevates Hudi's role from merely a table format to a comprehensive and robust data lakehouse platform.

Here are some of the key features of Hudi's lakehouse platform:

Apache Iceberg is another key technology that enables a lakehouse architecture, providing APIs and libraries that enable compute engines to interact with tables according to a specification. It introduces features essential for data lake workloads, including schema evolution, hidden partitioning, ACID-compliant transactions, and time travel capabilities.

Log Analytics is crucial for businesses to analyze security logs, monitor cloud infrastructure, and gain insights into user behavior. This is especially important for long-term use cases, such as analyzing application performance trends or investigating the root causes of security incidents.

Exporting data to specialized third-party tools can lead to significant egress and storage costs, particularly for organizations handling large volumes of log data.

ChaosSearch is a solution that simplifies log and event analytics within the Databricks Lakehouse, allowing businesses to keep data within cloud object storage and analyze it without transferring data out of the cloud. This significantly reduces costs and enhances the data lakehouse platform's observability capabilities in a cost-effective and efficient manner.

A data lakehouse is a powerful tool for handling big data, and its use cases are as diverse as they are impressive.

ByteDance used a data lakehouse to build an exabyte-level data storage system, which enabled them to provide real-time machine learning capabilities.

Notion scaled its data infrastructure by building an in-house lakehouse, which resulted in significant cost savings and faster data ingestion.

Halodoc's adoption of a lakehouse architecture helped them tackle challenges associated with managing vast healthcare data volumes.

This architecture enabled Halodoc to improve patient care through faster, more accurate decision-making and support both batch and stream processing for timely health interventions.

The data lakehouse architecture used by Notion employed S3 for storage, Kafka and Debezium for data ingestion, and Apache Hudi for efficient data management.

ByteDance's implementation of Hudi's Merge-on-read tables, indexing, and Multi-Version Concurrency Control features allowed them to provide instant and relevant recommendations.

The use of a lakehouse architecture by these companies has enabled them to handle rapid data growth and meet product demands, especially for AI-driven applications.

A data lakehouse offers a cost-effective storage solution by leveraging low-cost cloud storage options, reducing the need for managing multiple systems and significantly lowering overall engineering and ETL costs. This makes it an economical choice for startups and enterprises alike that need to manage costs without compromising on analytics capabilities.

Data in a lakehouse is stored in open file formats like Apache Parquet and table formats such as Apache Hudi, Iceberg, or Delta Lake, allowing various engines to concurrently work on the same data, enhancing accessibility and compatibility.

Here are some key benefits of a data lakehouse's storage and management capabilities:

Lakehouses can also enforce strict schema adherence and provide transactional consistency, which minimizes write job failures and ensures data reliability.

Data management is a crucial aspect of any organization, and it's essential to understand its importance. Data management is the process of capturing, storing, and processing data from various sources to make informed decisions.

Data management is important because it helps organizations make sense of the vast amounts of data they collect. This process involves data integration, which can be challenging, with 8 common integration challenges that can be overcome with the right strategies.

A data management team should include key roles such as data engineers, data scientists, and data analysts to ensure that data is properly managed and utilized.

A data lakehouse is a cost-effective solution for managing data, as it leverages the low-cost storage of cloud-based data lakes while providing sophisticated data management and querying capabilities similar to data warehouses.

By using a lakehouse, organizations can eliminate non-monetary costs associated with running and maintaining ETL pipelines and creating multiple data copies, further streamlining operations.

Data lakehouses also reduce the need for managing multiple systems, significantly lowering overall engineering and ETL costs.

Here are some key benefits of using a data lakehouse for cost-effective management:

Overall, a data lakehouse provides a cost-effective solution for managing data, allowing organizations to streamline operations and reduce costs associated with data management.

A secure and governed data storage system is crucial for any organization. This is where the Iceberg tables in Cloudera come in, integrating seamlessly with SDX to provide unified security, fine-grained policies, governance, lineage, and metadata management.

With this integration, you can focus on analyzing your data while Cloudera takes care of the rest. This means you don't have to worry about the nitty-gritty details of security and governance.

Here are some key benefits of a secure and governed data storage system:

By implementing a secure and governed data storage system, you can ensure that your data is protected and easily accessible when you need it. This will save you time and resources in the long run, allowing you to focus on more important tasks.