As a new data management architecture, data lakehouse combines the best elements of data lakes and data warehouses. Lakehouses are enabled by a new open and standardised system designed by implementing similar data structures and data management features to those in a data warehouse directly on the kind of low-cost storage used for data lakes. They are what you would get if you had to redesign storage engines in the modern world, now that cheap and highly reliable storage (in the form of object stores) are available. In a data lakehouse, you will have processing power on top of Data Lakes such as S3, HDFS, Azure Blob, etc.
In simpler terms, you don’t need to load the data onto any data warehouses to process and analyse business intelligence requirements. You can directly query the data underlying in your data lakes made of object storage. This method decreases the operational overhead on data pipelining and maintenance.
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Advantages of Data Lakehouse
Elimination of simple ETL (Extract, Transform and Load) jobs: A data warehouse is required to query or perform analysis on the data loaded into it. Using ETL/ELT tools, you can load the data from your existing data lake into your data warehouse by cleansing and transforming it into the destination schema. But with data lakehouse, the data lake will be directly connected to the query engine, eliminating the need for additional ETL.
Reduced Data Redundancy: Data lakehouse eliminates data redundancy. You may have data on multiple tools and platforms, such as cleaned data on a data warehouse for processing, metadata on BI tools, temporary data on ETL tools, etc. To prevent data integrity issues, these data must be maintained and monitored continuously. You can overcome this issue of data redundancy if you are using a single tool to process your raw data.
Ease of Data Governance: Data lakehouse can reduce the operational overhead associated with managing data governance across multiple tools. You must be careful when transferring sensitive data from one tool to another so that each tool maintains the proper access controls and encryption. Using one data lakehouse tool, however, you can manage data governance in a centralised manner.
Directly Connect to BI Tools: Using tools such as Apache Drill, data lakehouse enables direct access to some of the most popular business intelligence tools (Tableau, PowerBI, etc.). It eventually reduces the time taken from raw data to visualisation by exponential factors.
Cost Reduction: The data warehouse and data lake paradigm require processing data in multiple places at once, and the storage cost is also high. Comparatively, the data lakehouse can store the data in cheap storage like object storage such as S3, Blob, etc.
Available Tools in the Market
There are some good data lakehouse enabling tools by cloud vendors and open source communities. Here are some.
Google Bigquery
Google Bigquery is a modern data warehousing platform by Google Cloud Platform. With BigQuery (BQ), we can abstract the storage and computing layers completely, following the data lakehouse concept. Unlike other competitors, BQ pricing is determined by the amount of data processed per query, not by storage. Most organisations used Google’s BigQuery to adopt the data lakehouse concept without knowing the new paradigm.
Apache Drill
Apache Drill is a schema-free distributed query engine. Using this query engine, you can create a data lakehouse. It supports cross-format querying, such as joining JSON and CSV data using one query. A drill is an Apache open-source SQL query engine for exploring big data. The platform was built from the ground up to support high-performance analysis of the semi-structured and rapidly evolving data coming from modern Big Data applications while still enabling familiarity and ecosystem of ANSI SQL, the industry-standard query language. Drill offers plug-and-play integration with Apache Hive and Apache HBase deployments.
Amazon Athena
Amazon Athena is a managed service provided by Amazon Web Services. The Athena serverless query engine is based on Facebook’s presto. After creating the schema definition, the user can query the data directly from S3. You can use the AWS Glue crawler to automate schema discovery.
Delta Lake
Delta Lake allows leveraging the processing power of pre-built/pre-owned spark infrastructure. It enables the ACID methodology on distributed storage. Delta Lake was founded by the Spark and Databricks founders.
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Business Intelligence to AI
Lakehouse radically simplifies enterprise data infrastructure and accelerates innovation in an era when machine learning is poised to disrupt every industry. Most data that went into a company’s products or decision-making was structured data from operational systems. Many products incorporate artificial intelligence with computer vision and speech models, text mining, and other methods. Instead of a data lake, why use a lakehouse for AI? The lakehouse provides data versioning, governance, security, and ACID properties essential even for unstructured data.
Current lakehouses reduce costs, but their performance can still trail specialised systems (such as data warehouses) that have years of investments and real-world deployments behind them. In addition, some tools (BI tools, IDEs, notebooks) may be preferred over others, so lakehouses will also need to improve their UX and their connectors to popular tools so that they can appeal to a variety of personas. As the technology continues to mature and develop, these and other issues will be addressed. Eventually, lakehouses will fill this gap while remaining simpler, more cost-effective, and more suited to diverse data applications.
As the concept of data lakehouse is early, there are some limitations to consider before entirely depending on its architecture, such as query compatibility and data cleaning complexity. However, data experts can contribute to the issues and rules of open-source tools. Many of the more prominent companies, like Facebook and Amazon, have already set up a data lakehouse and opened-sourced the tools they use. Getting started with the Apache Drill on your laptop and connecting to your existing data lake can help you feel how data lakehouse works.
