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DataRavel - Early Access
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Your name
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Your data stack
Snowflake
Google BigQuery
AWS Redshift
DBT
Apache Airflow
Dagster
Mage
Coalesce
Apache Spark
Databricks
Atlan (or Colibra or similar)
Privacera (or similar data governance tools)
Soda (
soda.io
for data quality)
Apache Iceberg
ClickHouse
Google Dataproc
Google Dataflow
AWS EMR
AWS MWAA
Astronomer
Monte Carlo
Unravel Data
AWS Glue
What's the range for number of data pipelines your organization is dealing with?
<50
50-200
200-1000
>1000
Clear selection
Which are the issues that occur most repetitively in your current data stack setup?
Pipeline Failures
Downstream Dependencies
Table Values
Data Quality Challenges
Cross-team Dependencies
Surprise Costs
Sub-optimal Queries
Schema Evolution Challenges
Other:
How do data engineers go about fixing these repetitive issues?
Identify manually and address the issues in regular sprints
We have QA engineers working on our data platform
We have a few tools that help us identify issues early
Our developers are proactive and they fix the issues proactively without waiting to add them in sprints
Other:
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