8 Data Lake Vendors to Make Your Data Life Easier in 2023
Data has to be stored somewhere. Data warehouses are repositories for your cleaned, processed data, but what about all that unstructured data your organization is starting to notice? Where does it go? To make your data management processes easier, here’s a primer on data lakes, and our picks for a few data lake vendors worth considering.
What is a data lake?
First, a data lake is a centralized repository that allows users or an organization to store and analyze large volumes of data. This can be structured, semi-structured, and even unstructured data.
What makes data lakes unique is that, unlike traditional data storage systems, they can store data in its raw form without the need for pre-defined schemas or transformation. In short, data can be preserved as-is, which allows for the preservation of its aboriginal format and granularity.
Why should you use a data lake?
Where there are a few reasons. First, is scalability. Data lakes are designed to handle large volumes of data. This means they are well suited to scale horizontally by disturbing data across multiple storage nodes. This allows for the storage and processing of petabytes and even exabytes of data. Then there is flexibility. Data lakes are able to handle a diverse range of data types. From images, videos, text, and even sensor data. All of this without the need for transformation upfront.
Then, there’s data integration. A data lake can also act as a central hub for integrating data from various sources and systems within an organization. This is done by consoling data into a single location which allows for easier data sharing, and improved collaboration across teams and departments. Finally, exploration and analysis. Since data is left in its raw form within the data lake, it’s easier for data teams to experiment with models and analysis techniques with greater flexibility.
So let’s take a look at a few of the leading industry examples of data lakes.
The Azure Data Lake is considered to be a top-tier service in the data storage market. Not only does it work with existing IT investments to help with an organization’s data and management, but it also integrates with operational stores and data warehouses. And with the ability to handle high workloads, users can run high-powered analyses and store data at any size while bringing out the greatest value of a business’s data asset.
Similar to Azure, Amazon Simple Storage Service is an object storage service offering scalability, data availability, security, and performance. AWS also focuses on customers of all sizes and industries so they can store and protect any amount of data for virtually any use case, such as data lakes, cloud-native applications, and mobile apps while providing easy-to-use management features.
With a focus on security, Cloudera’s data lake services prides itself in providing world-class metadata governance and management for its clients. But that’s not where they end their services. For many enterprise-sized organizations, the ability to run compliance auditing is paramount, as many of these organizations must follow specific laws surrounding the information they house. Think of hospitals and other organizations that have a great deal of data that fall under certain legal protections.
Delta Lake is the first open-source data lakehouse architecture service on this list. It also has an impressive list of integrations such as Amazon Redshift, Kafka, Python, Java, trino, DataHub, and others. This is because Delta Lake focuses on a robust list of features such as Time Travel, which allows users to access/revert to earlier versions of data for audits, rollbacks, and more, and like others on the list, the ability to scale metadata.
Google Cloud’s Data Lake is an all-around data lake that specializes in cost-effective ingest, storage, and analysis of large volumes of diverse, and full-fidelity data. It offers other options such as the ability to perform the re-hosting of a data lake, cloud analysis of data from a data lake, and the ability to build a cloud-native data lake to help take the pressure off of local and native organizational resources.
Like Delta Lake, IBM’s data lake services offer an open-source solution while providing a multi-engine architecture that allows for the optimized warehousing of workloads while supporting all data types. It’s deployable anywhere due to its hybrid and multi-cloud environments, and finally, IBM offers a consistent metadata layer that is suppressed by multiple engines.
What Oracle offers is a big data service that is a fully managed, automated cloud service that provides enterprise organizations with a cost-effective Hadoop environment. This means that customers can easily create secure and scalable Hadoop-based data lakes that can quickly process large amounts of data with simplicity and data security in mind.
Snowflake is a cross-cloud platform that looks to break down data silos. This is done by supporting a variety of data types and storage patterns to provide maximum flexibility. Data engineers, data scientists, analysts, and developers across organizations can access governed structured, semi-structured, and unstructured data for a variety of workloads, without resource contention or concurrency issues.
Conclusion
Pretty neat right? As data continues to rapidly grow, the need for data lake services will also grow in concert. And if you’re interested in learning more, well we have great news! You’ll be able to check out some of these amazing companies and their representatives at ODSC Europe’s AI Expo & Demo Hall. Come and learn for yourself how you can take your data engineering, storage, and data lakes to the next level from some of the leading companies. So, what are you waiting for? Get your free Expo pass now!
If you’re interested in learning more about how you can use or build data lakes, then be sure to check out the data engineering track as part of ODSC Europe this June as well. Register now while tickets are 40% off so you can check out the below sessions:
- ML Governance: A Lean Approach
- Want End-to-End MLOps? Delta & Databricks Make This A Reality!
- How to Build Stunning Data Science Web Applications in Python — Taipy Tutorial
- Bringing AI to Retail and Fast Food with Taipy’s Applications
- Navigating the Complexities of Analytics in the Cloud: Enablers and Strategies for Success
- Build and Deploy PyTorch models with Azure Machine Learning
- Getting Up to Speed on Real-Time Machine Learning
- Upgrading your engine without stopping the car: How the FT is improving our ML deployment practice with minimum disruption
Originally posted on OpenDataScience.com
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