Data Governance Tools Resources

Articles, Glossary Terms, Discussions, and Reports to expand your knowledge on Data Governance Tools

Resource pages are designed to give you a cross-section of information we have on specific categories. You'll find articles from our experts, feature definitions, discussions from users like you, and reports from industry data.

Contents

Data Governance Tools Articles

Leveraging Data Governance Across Big Data Environments

Data is a critical asset to organizations today and the growing volume of diverse data generation is making data management and governance increasingly important and challenging. Managing the ever-increasing complexity in data sets has given rise to the concept of big data, but what exactly is big data, and why is it important for businesses?

by Isha Kaur

The Case for Multicloud Infrastructure Adoption

Putting all your eggs in one basket doesn’t always work out well. We’ve all done it, and when done in a business context (e.g., Cloudflare’s July outage that briefly tanked numerous web-based businesses), the consequences can be far-reaching and dire.

by Zack Busch

Data Governance Tools Glossary Terms

Data Orchestration

Data orchestration connects scattered data sources into automated pipelines ready for analysis. Learn how it works, its benefits, and best practices.

by Shreesh Singh

Data Governance

Data governance refers to actions companies take to ensure data is reliable, accurate, and accessible. Learn more about its benefits and best practices.

by Sagar Joshi

Data Governance Tools Discussions

Question on: Rokt mParticle

May you have created some tutorials for beginners?

When I started using indicative, I needed a lot of help to understand all the features, I didn't find many tutorials, like explanatory videos in a more lay language. it would be really cool if you did them.

Hi José, thanks for reaching out with your question. You can find some of our tutorial-related content by exploring our product demo from the home page of our website (https://www.indicative.com/), as well as by visiting our support documentation (https://support.indicative.com/hc/en-us) that goes into great detail about each tool and feature we have to offer. If there was something else you were looking for or have ideas around new content you'd like to see - please do not hesitate to let us know by accessing the chat feature at the bottom-right of our website. Hope that helps!

Answered: Tara McQuaide on March 10, 2021

Your answer

Question on: Twilio Segment

I used segment in different organizations as I was the one suggesting it, but then

Is there any affiliate/partner program?

There is if you become a referral partner, but is an involved process with approval. Overview: https://segment.com/partners/channel/ Details: https://assets.ctfassets.net/9u2t3ap6pctb/t3XzV1NfUBFbLQ4IcMqeb/dbd73a9b6eb7926b348fcea2b1ce48a4/Segment_Select_Channel_Partner_Deck__1_.pdf

Answered: Andy Baird on November 22, 2019

Your answer

Question on: Databricks

Is Databricks worth it for data engineers managing big data processing?

A question for data engineers who are responsible for building and maintaining production ETL pipelines, managing data quality across large datasets, and keeping infrastructure costs in check — is Databricks actually worth the commitment, or do the cluster management complexity and DBU cost unpredictability offset the platform integration benefits?

Feature: Databricks unifies Spark-based processing, collaborative notebooks (Python, SQL, Scala), Delta Lake for ACID-compliant table management, Workflows for job scheduling, and Unity Catalog for governance — in one platform. Delta Lake improves pipeline reliability through schema enforcement and time travel for debugging, the notebook environment makes step-by-step transformation testing immediately visible without deploying separately.

Integration: The platform connects to cloud storage, data warehouses, Git, and third-party tools as part of the ETL workflow. SnapLogic lands data in Bronze and Databricks handling Silver and Gold transformation, with serverless compute and Unity Catalog managing governance without separate tooling.

Some big data processing and distributions software alternatives worth comparing:

Snowflake: Data engineers on SQL-first teams credit elastic virtual warehouse scaling and compute-storage separation for handling demanding workloads without one job affecting another. No native notebook environment, but strong SQL Worksheet and partner integrations.
Google Cloud BigQuery: Serverless model with no cluster management overhead is the most consistently cited data engineer benefit. Free tier for up to 1TB of queries per month.
Amazon EMR: For data engineers already deep in the AWS ecosystem, EMR runs Spark, Hadoop, and Hive workloads on managed clusters with tight IAM, S3, and Glue integration..
Azure Synapse Analytics: For Azure-native data engineering teams, Synapse combines SQL-based data warehouse, Spark pools, and Data Factory pipeline orchestration in one workspace.

The honest read: Databricks earns its place for data engineers managing complex, multi-stage Spark pipelines where the Delta Lake reliability, notebook collaboration, and ML-to-engineering workflow continuity justify the learning curve on cluster and compute configuration.

Would value input from data engineers who have used Databricks in production for more than six months. What was the first cluster configuration mistake that taught you the most about cost management, and at what pipeline complexity did the Delta Lake + Unity Catalog combination start delivering a noticeable reliability improvement over your previous stack?

The "worth it" answer probably hinges on something not mentioned yet: whether the same team spans both exploratory ML work and production ETL, or whether those are two separate teams in your org. Databricks' pitch is largely about continuity between notebook exploration and production pipelines. If data engineering and ML rarely touch each other's work, that continuity benefit goes mostly unused, and you're left paying the DBU premium and cluster overhead for a unification story that doesn't match how your org is actually structured.