Securing and Auditing the Data Lake

Securing and Auditing the Data Lake

Security is absolutely critical in data lakes, since a data lake oftentimes contains the majority of an organization’s data. Giving users access to the data they need to do their job should not necessarily mean giving them access to all of the data in the lake. Unfortunately, many data lake tools, especially custom do-it-yourself (DIY) implementations, do not provide an easy way to manage and audit data access.

In Magpie, security is a core concept, where every user action is verified and logged for future review. In this post, we’ll demonstrate how to secure your data lake quickly and effectively with Magpie.

From Zero to Data Lake with Silectis Magpie

From Zero to Data Lake with Silectis Magpie

More and more companies are turning to data lakes as a way to unify and get value out of their growing collections of data. However, it can be a challenging to navigate the ever-changing technology landscape around these lakes, set one up, and quickly get value from it.

In this post, we’ll walk through a technical tutorial of how Magpie can help companies get up and running with a data lake quickly. We’ll show how companies can easily configure and explore a set of enterprise data sources, enrich that enterprise data with third party sources, and perform initial analysis.

Data Lake Storage: Choosing the Right Tool for the Job

Data Lake Storage: Choosing the Right Tool for the Job

Which storage type is most performant? JSON? Parquet? ORC? Avro? It depends!

In this blog, we get our hands dirty seeing which data storage formats work best for common use cases. We tell you which tools work best and include real benchmark examples for each.