DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports Events Over 2 million developers have joined DZone. Join Today! Thanks for visiting DZone today,
Edit Profile Manage Email Subscriptions Moderation Admin Console How to Post to DZone Article Submission Guidelines
View Profile
Sign Out
Refcards
Trend Reports
Events
Zones
Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
  1. DZone
  2. Data Engineering
  3. Big Data
  4. Intelligent Big Data Lake Governance

Intelligent Big Data Lake Governance

Governing such large amounts of data is certainly a challenge, but it can be done. Read on to see how one team of data engineers approached the problem.

Mamta Chawla user avatar by
Mamta Chawla
·
May. 02, 19 · Analysis
Like (3)
Save
Tweet
Share
7.66K Views

Join the DZone community and get the full member experience.

Join For Free

When you have data, and data which is flowing fast with variety into the ecosystem, the biggest challenge is governing that data. In traditional data warehouses, where data is strucured and the structure is always known, creating processes, methods, and frameworks is quite easy. But in a big data environment, where data flows fast while inferring run time schema, the need to govern data is at run time.

When I was working with my team to develop an ingestion pipeline and collecting ideas from the team and other stakeholders on how the ingestion pipeline should be, one idea was common: can we build a system where we can analyze what changed overnight in a feed structure. The second requirement was finding the pattern of the data, e.g. how could we find out that a data element was a SSN numer, a first name, etc., so that we can tag the sensitive information at run time?

The core data governance team was struglling with how to maintain information about each and every element from all the feeds, as in a data lake there are more than 3,000 feeds. And information about each and every elemnt in those 3,000 feeds was equivalent to maintaing the information of more than 100,000 elements. That's a quite a lot of work; is there any way to do it? I had a thought that if I needed to build an ingestion frameowerk for an ecosystem like Hadoop, where updates are challenges, data governanace methods and rules should be applied during ingestion only. This problem needed to be solved for structured and unstratucred feeds. In any organization, the bigger part of data is structured.

For structured data, we have built a metadata repository-based ingestion frameowrk. Before ingestion,  MySQL-based feeds of metadata, elements' metadata, user roles and access metadata, and SLA metadata are used to fill in the metadata repositry. For each and every element in the metadata repository, data governance rules and methods (PII, master data, critical data element, pattern matching, business rules) are defined.

Once all the metadata is defined, on the arrival of the feed, it gets ingested as per the defined rules. During ingestion, all the rows were identified with valid and invalid elements. And for each row a trust factor was created, which was the indicator of how much the row can be trusted on a scale from 1 to 100. If it's 70, that means 70% of the elements in the row could satisfy the data governanace data quality rules.

Big data Data lake

Opinions expressed by DZone contributors are their own.

Popular on DZone

  • How to Create a Real-Time Scalable Streaming App Using Apache NiFi, Apache Pulsar, and Apache Flink SQL
  • Tech Layoffs [Comic]
  • The 31 Flavors of Data Lineage and Why Vanilla Doesn’t Cut It
  • Deploying Java Serverless Functions as AWS Lambda

Comments

Partner Resources

X

ABOUT US

  • About DZone
  • Send feedback
  • Careers
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 600 Park Offices Drive
  • Suite 300
  • Durham, NC 27709
  • support@dzone.com
  • +1 (919) 678-0300

Let's be friends: