Data lakehouses combine the data warehouse and data lake concepts for businesses.
These tools let you build cost-effective data storage solutions by combining the management capabilities of data lakes with the data architecture found in data warehouses.
Additionally, there is a reduction in data migration and redundancy, less time is spent administrating, and shorter schema and data governance procedures actually become a reality.
One data lakehouse has many advantages compared to a storage system with several solutions.
These tools are still used by data scientists to improve their understanding of business intelligence and machine learning procedures.
This article will take a quick look at data lakehouse, its capabilities, and the available tools.
Introduction to Data Lakehouse
A new kind of data architecture called a “data lakehouse” combines a data lake and a data warehouse to address the weaknesses of each one independently.
The lakehouse system, like data lakes, uses low-cost storage to keep huge amounts of data in its original form.
The addition of a metadata layer on top of the store also provides data structure and empowers data management tools similar to those found in data warehouses.
It contains massive amounts of structured, semi-structured, and unstructured data obtained from the various business applications, systems, and devices utilized throughout the enterprise.
As a result, unlike data lakes, the lakehouse system can manage and optimize that data for SQL performance.
It also has the ability to store and process large amounts of diverse data at a cheaper cost than data warehouses.
A data lakehouse comes in handy when you need to execute any data access or analytics against any data but are unsure of the data or the recommended analytics.
A lakehouse architecture will function quite well if performance is not a primary concern.
That does not imply that you should base your entire structure on a lakehouse.
More information on how to select a data lake, lakehouse, data warehouse, or specialized analytics database for each use case can be found here.
Features of Data Lakehouse
- Concurrent data reading and writing
- Adaptability and scalability
- Schema assistance with data governance tools
- Concurrent data reading and writing
- Storage that is affordable
- All data types and file formats are supported.
- Access to data science and machine learning tools that is optimized
- Your data teams will benefit from having access to just one system to transfer workloads through it more quickly and accurately.
- Real-time capabilities for initiatives in data science, machine learning, and analytics
Top 5 Data Lakehouse tools
Databricks, which was founded by the person who first developed Apache Spark and made it open source, provides a managed Apache Spark service and is positioned as a platform for data lakes.
The data lake, delta lake, and delta engine components of the Databricks lakehouse architecture enable business intelligence, data science, and machine learning use cases.
The data lake is a public cloud storage repository.
With support for metadata management, batch and stream data processing for multi-structured datasets, data discovery, safe access controls, and SQL analytics.
Databricks offers most of the data warehousing functions one might expect to see in a data lakehouse platform.
Databricks recently unveiled its Auto Loader, which automates ETL and data input and leverages data sampling to infer the schema for a variety of data types, in order to deliver on the essential components of the data lake storage strategy.
Alternately, users can build ETL pipelines between their public cloud data lake and Delta Lake using Delta Live Tables.
On paper, Databricks appears to have all the advantages, but setting up the solution and creating its data pipelines requires a lot of human labor from skilled developers.
At scale, the answer also becomes more complex. It’s more complicated than it seems.
A data lake is a single, central location where you can store whatever type of data you choose at scale, including unstructured and structured data. AWS S3, Microsoft Azure, and Google Cloud Storage are three common data lakes.
Data lakes are incredibly well-liked because they are very affordable and simple to use; you can essentially store as much of any type of data as you like for very little money.
But the data lake doesn’t offer built-in tools like analytics, query, etc.
You need a query engine and data catalog on top of the data lake (where Ahana Cloud comes in) to query your data and use it.
With the best of both the Data Warehouse and the Data Lake, a new data lakehouse design has developed.
This indicates that it is transparent, adaptable, has good price/performance, scales like a data lake supports transactions, and has a high level of security comparable to a data warehouse.
Your high-performance SQL query engine is the brains behind the Data Lakehouse. Because of this, you can execute high-performance analytics on your data lake data.
Ahana Cloud for Presto is SaaS for Presto on AWS, making it incredibly simple to start using Presto in the cloud.
For your S3-based data lake, Ahana already has a built-in data catalog and caching. Ahana gives you Presto’s features without requiring you to handle the overhead because it does it internally.
AWS Lake Formation, Apache Hudi, and Delta Lake are just a few of the transaction managers that are part of the stack and integrate with it.
Organizations seek to quickly, simply, and efficiently evaluate massive amounts of rapidly rising data.
Dremio believes that an open data lakehouse combines the benefits of data lakes and data warehouses on an open basis is the best approach to accomplish this.
Dremio’s lakehouse platform provides an experience that works for everyone, with an easy UI that allows users to complete analyses in a fraction of the time.
Dremio Cloud, a fully managed data lakehouse platform, and the launch of two new services: Dremio Sonar, a lakehouse query engine, and Dremio Arctic, an intelligent megastore for Apache Iceberg that delivers a unique Git-like experience for the lakehouse.
All of an organization’s SQL workloads can be run on the frictionless, endlessly scalable Dremio Cloud platform, which also automates data management tasks.
It is built for SQL, offers a Git-like experience, is open source, and is always free.
They created it to be the lakehouse platform that data teams adore.
Utilizing open source table and file formats like Apache Iceberg and Apache Parquet, your data is persistent in your own data lake storage when using Dremio Cloud.
Future innovations can be easily adopted, and the proper engine can be chosen based on your workload.
Snowflake is a cloud data and analytics platform that can meet data lakes’ and warehouses’ needs.
It began as a data warehouse system built on cloud infrastructure.
The platform comprises of a centralized storage repository that sits on top of public cloud storage from AWS, Microsoft Azure, or Google Cloud Platform (GCP).
Following that is a multi-cluster computation layer, where users can launch a virtual data warehouse and conduct SQL queries against their data storage.
The architecture allows for decoupling storage and computation resources, allowing organizations to scale the two independently as needed.
Finally, Snowflake provides a service layer with metadata categorization, resource management, data governance, transactions, and other features.
BI tool connectors, metadata management, access controls, and SQL queries are just a few of the data warehouse functionality that the platform excels at offering.
Snowflake, however, is restricted to a single relational SQL-based query engine.
As a result, it becomes simpler to administer but less adaptable, and the multi-model data lake vision is not realized.
Additionally, before data from cloud storage can be searched or analyzed, Snowflake requires businesses to load it into a centralized storage layer.
The manual data pipelining procedure necessitates prior ETL, provisioning, and data formatting before it can be examined. Scaling up these manual processes makes them frustrating.
Another option that appears to be a good fit on paper but in fact, deviates from the data lake principle of simple data input is Snowflake’s data lakehouse.
Modern, open architecture known as a “data lakehouse” makes it possible to store, comprehend, and analyze all of your data.
The most well-liked open source data lake solutions’ breadth and flexibility are combined with the strength and depth of data warehouses.
The newest AI frameworks and prebuilt AI services can be used with a data lakehouse on Oracle Cloud Infrastructure (OCI).
It is feasible to work with additional types of data while using an open-source data lake. But the time and effort required to manage it could be a persistent drawback.
OCI offers fully managed open source lakehouse services at lower rates and with less management, allowing you to anticipate lower operational expenses, better scalability and security, and the capacity to consolidate all of your existing data in one location.
A data lakehouse will increase the value of data warehouses and marts, which are essential to successful enterprises.
Data can be retrieved using a lakehouse from several locations with just one SQL query.
Existing programs and tools receive transparent access to all data without requiring adjustments or acquiring new skills.
The introduction of data lakehouse solutions is a reflection of a larger trend in big data, which is the integration of analytics and data storage in unified data platforms to maximize business value from data while lowering the time, cost, and complexity of value extraction.
Platforms including Databricks, Snowflake, Ahana, Dremio, and Oracle have all been linked to the idea of a “data lakehouse,” but they each have a unique set of features and a tendency to function more like a data warehouse than a true data lake as a whole.
When a solution is marketed as a “data lakehouse,” businesses should be wary of what it actually means.
Enterprises need to look beyond marketing jargon like “data lakehouse” and instead look into each platform’s features to select the best data platform that will expand with their businesses in the future.