The modern enterprise taps into over 400 different data sources to extract the insights that sharpen its competitive edge.
The complexity, though, does not stop at the origin, where data is generated.
To get valuable insights from raw data enterprises must extract data from its source, transform the data (clean and aggregate it), and finally load the data into a data warehouse or BI tool, where it is served to data scientists for analysis.
The ETL (extract, transform, load) data pipelines require the expert knowledge of data engineers to set up and maintain.
Without a data lineage tool, which shines a light on the data flows through the complex ecosystem of interdependent data flows, enterprises would be flying blind.
A data lineage tool is software that allows you to view and inspect, well, the data lineage.
So, the question is what is data lineage?
It is a mapping of the data lifecycle as it moves from its source to its final destination.
The data journey should specify multiple entities:
The data journey - or if you prefer, the flow of data - should be inspectable throughout the lineage.
That is, data lineage tools provide full visibility of data changes via audit trails that showcase in fine granularity each touchpoint and how it changed the data.
Data lineage software’s capturing capabilities allow an in-depth introspection into the data assets and how they changed through time: the person, who accessed the data, the person or process, who initiated data changes, and give the auditor the possibility to replay the events, which led to the state you are inspecting.
So, why do enterprises turn to data lineage tools?
Multiple necessities and benefits are driving the adoption of data lineage software:
Unlock the advantages of data lineage for your enterprise data by choosing the right tool for your company.
Keboola is the end-to-end data operations platform. With Keboola you can automate your entire data integration pipeline: from collecting data, transforming it, and storing it for analysis.
At each step of the pipeline, Keboola automatically tracks all relevant metadata and constructs logs, which gives you a granular view of data lineage.
Each operation on the data platform is extensively tagged with operational metadata describing the user activity on the events level, job activity (for automated workloads), the schema evolution throughout the changes, compliance with security rules, and even monitors for optimal pipeline performance.
All the metadata collection is done automatically. You do not need to waste any energy building up the data lineage, or integrating with any additional tools. Simply perform your usual data operations, and data lineage comes out of the box.
Because Keboola does not just offer metadata, you can tap into its rich resources for any needs you have from end-to-end, such as:
Take Keboola for a spin. Keboola has an always-free, no-questions-asked plan. So, you can explore all the power Keboola has to offer. Feel free to give it a go or reach out to us if you have any questions.
Dremio is a querying engine that allows you to access your data in data lakes, such as on Amazon Web Services (AWS) or Microsoft Azure.
Dremio establishes data lineage by constructing a data graph, where the relationship between your data sources, virtual datasets, and all your queries are maintained.
Dremio’s advantage is that it is easily used by non-technical personnel when constructing query-like tasks.
Its main disadvantage is that Dremio was built as a querying software, so does not offer out-of-the-box flexibility and maturity of features that comes from data operations platforms. For example, Dremio has hiccups when integrating from the data sources within the platform to other BI tools, as well as limited data governance and auditing features.
Kylo is an open-source data lake management software platform.
Kylo is rather versatile. It allows users to ingest, cleanse, validate, profile, and wrangle (in SQL) data lakes data, as well as monitor it.
Kylo maintains a rich metadata store that automatically fills in from the data tables. Users can access field- and table- metadata by querying, as well as visually inspect data provenance.
A common shortcoming is that Kylo necessitates specialized engineering skills to understand and use it. It can be thought of as an accelerator for data engineers, who want to build their own data lineage, not for non-technical users.
Octopai is a different data lineage tool from the ones presented above. Instead of centralizing on a data operation task (end-to-end, querying, ingestion, …), Octopai does just data lineage.
It connects to your data storage or BI environment and extracts metadata to build data lineage insights.
An obvious advantage of Octopai is that it does just this one thing and it does it well.
The disadvantage of Octopai is that because its features are limited to data lineage, business glossary, and data discovery, it does not build other tools that would pair well with data governance, such as access control, security monitoring, etc. Additionally, being rather novel, Octopai has the shortcoming of integrating with only a selected few technologies.
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