We recently covered an article on AWS Lake Formation and how it is going to make dealing with big data and large databases quite easy. While it recently announced the general availability of Lake formation to help developers, it’s not the only data lake available for developers to run their analytics and machine learning algorithms.
In this article, let us check out a few alternatives of AWS Lake Formation that have been enabling data scientists to deal with large data volumes.
The importance of data lakes has increased dramatically in recent years to capture every aspect of your business operations in data form. As a result, there is petabytes and even exabytes of data that companies have to deal with storing and computing. This is where data lakes come into the picture and many companies are providing economical ways of storing the data.
One of the most popular resources, Azure data lake has all the capabilities that are required for it to make easy for developers and data scientists to store data of any size, shape and speed. It allows for all types of processing and analytics across platforms and languages while removing complexities of ingesting and storing data. It integrates seamlessly with operational stores and data warehouses allowing a user to extend current data applications.
Google Cloud Platform (GCP) allows storage, processing and analysis of massive amounts of data in a cost-efficient and agile way. Cloud Storage as the data lake is well suited to serve as the central storage repository for many reasons such as its performance & durability, consistency, cost efficiency, security, flexible processing, allowing a central repository among others.
Capgemini along with Dell EMC created a business data lake to enable next-gen information management solution that delivers big data to all users. Combining Capgemini’s leading business processes and big data skills with Dell EMC’s cutting edge technology, this data lake as a service addresses companies’ insights, data and information challenges. It allows for gathering and accessing big data on the cloud and then harnessing the power of data science to accelerate the business’s journey to becoming an insights-driven organisation.
In partnership with Cloudera, IBM offers enterprise-grade products and services to help build a data lake and then manage, govern, access and explore big data. It provides a cost-effective way for users to integrate enterprise-grade open source technology with real-time analytics capabilities. It is particularly useful for unanalysed data to help in making smarter, more agile, data-driven decisions.
The company recently announced the Data Lake Edition for Oracle Analytics Cloud (OAC) which allows developers to get easy access and enable them with storing big data. The new Oracle Analytics Cloud lets business analysts explore all data in data lakes and blend it with personal, enterprise, or external sources. It allows easy discovery of new insights, rapid enhancement and enriching data sets to create data flows that scale to the needs of the project.
The Atos Codex Data lake Engine by the company certified by Cloudera provides an end-to-end data management and security platform with optimal scalability and cost-effectiveness. It enables businesses to store, manage, govern and analyse complex data with ultimate security and control. It includes a comprehensive data management software, based on Cloudera Enterprise that enables machine learning and analytics optimized for the cloud.
It has a data lake management software platform that aims at addressing common challenges in data lake implementation while enabling best practices and driving data lake adoption beyond engineers. It offers open source Kylo project under the Apache 2.0 license which is a data lake management software platform allowing data ingestion, data preparation while enabling metadata management, governance, security and best practices.