Data Catalogs and Data Lakes

23 Mar, 2022 •

environmental technology concept. sustainable development goals. sdgs.

In this era of big data, organizations and decision-makers are drowning in data but starving for insights. The need for real-time insights in today’s fast-paced and ever-changing world has called for accelerated business intelligence insights. The sheer quantity of data and the dynamic field has raised unique challenges pertaining to the speed and accuracy of insights.  

The need to have identifiable data that can generate actionable insights is the key to driving utility from data assets and resources. But, before defining how to make data actionable, it is important to define how to make it discoverable and usable. In the present business landscape, large quantities of data are stored in soled data structures or centrally shared enterprise resources like data lakes, data warehouses, etc. One of the most common and widely used central repositories is data lakes. Businesses invest thousands of dollars in subscriptions services, software, and consultants to never realize the complete utility and usability of their data assets.  

What is a data lake?  

First coined by James Dixon, CTO of the business intelligence software platform Pentaho, the term data lake is believed to have been used to contrast this form of storage with a data mart. A data lake is a centralized repository that allows one to store structured, semi-structured, and unstructured data at any scale. In contrast to relational database systems, which require rigid definitions of schema, Data lakes support various schemas and don’t require any specific definitions upfront. By providing a central repository of data, data scientists, engineers and analysts have a central asset to access, transform and derive insight from their data assets.  

Popular examples of data lakes include – Amazon S3, Google Cloud Storage, Apache Hadoop, etc.  

Now that we’ve touched on data lakes and their wide usage throughout data storage projects, let’s talk about the main problems that business users and data stewards face when using data lakes:  

  1. No view into ownership, the interrelationship of data assets  
  2. Data privacy and security risks  
  3. Metadata( Data about the data) is not available  

Data Catalogs help tackle these challenges to empower data Lake users towards improving functionality:  

What is a Data Catalog?  

A data catalog is an organized inventory of data assets in the organization. A catalog performs the following functions for your data assets:  

  1. Centralizes your data sources and BI Reports   
  2. It can store additional information about the data – its profile, characteristics, and lineage  
  3. It allows for enrichment and curation of data – by classifying, linking, and tagging data  
  4. It allows for a common platform for analytics, business SMEs, Data Engineering, IT to collaborate on data  

The graphic below clearly depicts how data catalogs can help improve categorize and inventory of data sources for ease of usage. 

How does a data catalog empower your data lakes?  

1. Effective metadata management:  

Metadata ensures that data can be found, used, preserved, and reused as and when required. Data Catalogs help connect metadata across data lakes, data siloes, etc. Modern data catalogs even support active metadata which is essential to keep a catalog refreshed. Active metadata is Machine learning augmented metadata that enables actionable insight from metadata. Data in a data lake is transformed and operated on extensively, by making it easily discoverable and usable, data catalogs, speed up the process of discovery.  

2. Security capabilities:  

Data catalog helps focus on security governance, risk management, and compliance. This includes encryption at rest and in transit, network security and server hardening, administrative access control, system monitoring, logging and alerting, and more By only harvesting metadata, and using PII identification and masking algorithms when scanning data. It ensures, that PII information is not extracted and also ensures compliance with privacy guidelines.   

3. Data profiling and lineage:  

Outlining all data assets and providing a summary about data assets and types is very useful to quickly discover and identify. Data catalogs also have the powerful ability to describe how data assets change over time through data lineage functionality. Moreover, by providing valuable insight into quality and governance, data catalogs can help ensure the appropriate use of data assets  

dvsum anim data intelligence ml data catalog tools

Data catalogs are the key tools that empower data lakes.   

Share this post:

You may also like