Compherending Data Catalog with Data Discovery

Data Catalog with Data Discovery | The In-depth Guide

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Introduction to Data Catalog and Discovery Platform

As companies are being cautious about data, they are investing more to leverage data to digital power products, drive decision-making, empower decision-making, and understand the importance of the health and reliability of critical assets. While building a data platform, both data must be organized, centralized, and discoverable. It is important that data is cataloged, accurate, clean, and observable for ingestion to achieve this.

When Data Discovery meets Data Catalog?

Data Catalog + Data Discovery = Data Catalog 2.0

Data catalogs work best with rigid models, but with the increasing complexity of data pipelines, complex unstructured data becomes an essential standard for understanding our data. This is where the data discovery platform plays a crucial role by providing a dynamic understanding of data. It shows How data is being ingested, stored, and used by the consumers. Data catalog, along with data discovery, provides superior accessibility and real-time understanding of the data. It can map the difference between the current state and the ideal state.

Questions we ask in the data discovery process.

  1. What is the most recent dataset? Can any data sets be deprecated?
  2. When was any table updated?
  3. What is the meaning of a given field in my domain?
  4. Who is authorized to access this data? When was this data used? Who used this data?
  5. What are the upstream and downstream jobs of this data?
  6. Is data is passing environment quality benchmarks?
  7. What data matters for my business requirements?
  8. What are my assumptions about this data, and are they being met?

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How to select what platform you need?

What platform to select can be answered by a simple question about how we help users find the data they need and how secure it is. Some of the ways are

Find data by Search

All the platforms allow users to search for table names based on keywords. Some platforms enhance their approach even further by giving extensive table descriptions and user descriptions. Once discovered, the data can be reverted based on popularity.

Find data by Recommendations

Recommendation-based tables are one of the ways to provide the given data for the table. This may act as a homepage. The recommendation can be suggested based on popular tables within a team/organization or recently used tables, or by providing the most queried data by the current user.

Find data by Free Text

Search terms can be parsed on the basis spacy-based library. Then tables candidates can be generated based on data graphs, and elected candidates can be ranked based on users all this together can give the ability to parse data with natural language queries.


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Need for Data Catalog and Data Discovery Platform

The need for a Data Catalog and Data Discovery Platform is mentioned below:

Asset Collection

Users can catalog different kinds of data logically across platforms in various collections to support business definitions and use cases. The platforms make it easier to classify search and share corporate knowledge, thereby increasing the efficiency of finding the right, which provides insights to address business challenges.

Data Profiling

The data catalog and discovery platform helps understand the real meaning of your data and its meaning, origin, or point of ingestion. Data profiling helps analyze key metrics and classify sensitive personal information resulting in better insights and classified-based protection, thus answering questions like What is the most recent dataset? Can any data sets be deprecated?

Lineage and Impact

Increases data reliability by encapsulating the origin and accentuating the process that created it, thus giving a transparent process by showing how data is being used, how it travels through the pipeline, what the impact on downstream tables answering questions like Who is authorized to access this data is? When was this data used? Who used this data? What are the upstream and downstream jobs of this data?


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Security and Classification

Data assets can be organized into categories and curated for faster and easier discovery this can also help in advanced security and governance. Automation ensures that compliance rules are applied consistently at the derived data set. This, in return, answers questions like Who is authorized to access this data? When was this data used? Who used this data?

Audit and Monitoring

Dashboards and metrics provide insights into data usage. The insights can provide the access pattern, trends and help direct alert where needed. They can also be used for alerting data stakeholders to potential unauthorized access/usage of data.

Business Glossary

View your business data and help get the right information to users using natural language queries. We can give business meaning to the data set by categorizing terms from hierarchically glossary vocabulary.

Comparison of different Data Catalog and Discovery Platforms

 

Search

Recommendations

Schemas & Description

Data Preview

Column Statistics

Space/cost metrics

Ownership

Top Users

Lineage

Change Notification

Supported Sources

Amundsen

✔️

✔️

✔️

✔️

✔️

 

✔️

✔️

   

Hive, Redshift, Druit, RDBMS, Presto, Snowflake

Datahub

✔️

 

✔️

     

✔️

✔️

✔️

 

Airflow ,Data warehouse (Snowflake, BigQuery, etc),

RDBMS

Metacat

✔️

 

✔️

 

✔️

✔️

       

Hive, RDS, Teradata, Redshift, S3, Cassandra

Atlas

✔️

 

✔️

         

✔️

✔️

HBase, Hive, Sqoop, Kafka, Storm,Spark,s3

Marquez

✔️

 

✔️

         

✔️

 

S3, Kafka,Airflow,spark,bigquery

Conclusion

In conclusion, Data discovery and cataloging enlighten us with what we have and how it relates. Together they discover relationships between different data sets and help us join the dots between them. They provide a reservoir of information about data assets of what it contains, where it is most relevant or who might have access to it. Both jointly act as vocabulary to the business logic for the data.

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