Authors: Sai Charan, Mahima Khandewal, Ruchir Bhardwaj, Sudhendu Pandey
With the advancement of technology and its application in various domains, the volume of data has grown tremendously. Managing and gaining meaningful insights from this huge data becomes difficult. And so, Industry-leading businesses increasingly rely on data analytics to drive strategic insights and gain a competitive edge. To do so, one needs a clear picture of all the data assets used to drive the business.
Here we see the need for an organized inventory of data assets in a centralized location with search benefits.
By organizing data from multiple sources into a searchable, centralized platform, data catalog tools enable data teams and other data consumers to locate, understand and utilize data more quickly and efficiently.
A Data Catalog is a collection of metadata that allows data consumers to quickly search an organization’s entire data landscape, along with data management and governance. It serves as an inventory of available data and provides information to evaluate fitness data for intended uses.
Data Catalog for Snowflake
Snowflake is a fully managed SaaS Data warehouse which enables data storage, processing, and analytic solutions that are faster, easier to use, and far more flexible than traditional offerings. It provides its users with many benefits, including Security and Scalability. Due to this, organizations are moving their data from traditional Data Storage to Snowflake.
We need a data catalog for data quality monitoring to get relations between tables and understand the flow of data with the help of data lineage.
Features of Data Catalog:
Search and Discovery.
Low Data Integration Costs.
Faster Access to Fresh Data
Data Quality Monitoring
Important Features of Data Catalog
Data governance is a set of standards, policies, and processes to store, manage and collect data for better decision-making. Without governance, organizations cannot ensure integrity, relevance, security, availability, and usability of organization’s data.
There are no set rules or checklists you need to follow to create a strong data governance. Each organization will have a different strategy to create their data governance which will depend on the organization's work and data. For example, an organization which deals with banking information will have to conform to its data access and retention policies as per financial regulations.
Data Governance defines how data is used by your organization. It isn't just about compliance, nor should it just be a headache for your IT teams. So, finding the right balance between data democratization and data governance can help create a strong, sustainable data culture in your organization.
Data governance matters because it helps companies answer critical questions like:
How do you store data assets?
Which assets are more valuable?
What are the relationships between various assets?
What is the monetary value of assets?
How can we protect and reuse assets?
Who has access to which asset?
And for any data to comply with regulatory standards, it must :
Facilitate lineage tracking back to the source
Record metadata along with its context in a data dictionary or a catalog
Ensure data quality monitoring and reporting
Create, enforce and document access policies
Data lineage reveals evolution of data through its lifecycle - where data has come from and how it is enriched. It traces back the sources from where the data was derived and the transformational steps it went through.
The main objective of a good data lineage is to make it easy to track the data’s origins. It should give the list of changes data went through and the name of the tables from which the data originated. A clear visual flow and contextual information for each step help the user understand the entire data process from source to destination.
Most importantly, for all kinds of data users (not just the IT team) - a good data lineage should become the go-to place to learn about an organization’s data. Hence, it should be easy to access and navigate. This makes troubleshooting quicker and checking for data quality easier. It is also essential for meeting regulatory requirements for the traceability of calculations and data preparation. As such, it should be considered an essential part of any data catalog solution.
Benefits of implementing data lineage in your organization:
Spot data quality errors.
Identify the root cause of issues.
See the impact of any changes.
Easier auditing and documentation
Better data governance
A metadata catalog is a collection of all the data about your data. Metadata includes details such as the data source, origin, owner, and other attributes of a data set. A truly powerful metadata catalog will help you in creating a central repository for all your data and metadata - including the quality, structure, definitions, and usage of the data.
In a good data catalog, you can freely add additional metadata, tag your terms with things like a data category (e.g., sensitive, GDPR, PII related, track business owners) and any other important information. They also enable the management of any kind of metadata, not only about data but also about things like reports, APIs, servers, or anything else in your landscape.
It is essential to support custom metadata attributes that enrich data sets with them; these could be attributes like “department,” “business,” “data steward,” “ dataset,” or anything that makes sense for your organization.
A checklist of things you need to implement to ace your metadata catalog needs:
Understand the fine print and quality of your data.
Crowdsource your metadata catalog.
Get critical business context on your data.
Search through petabytes of data.
A data profiler, after analyzing your uploaded data, generates information about data patterns, numeric statistics, data domains, dependencies, relationships, and anomalies. Companies can then use this information to evaluate their data sets (or even single columns within the set through column profiling) and proceed with the data initiative at hand.
Data Profiler provides essential information about any dataset or data source so anyone can benefit from its implementation. Whether it’s a simple data analysis or something complex like building a data quality program, a data migration, designing or reviewing architecture, or creating a master model.
From data profiling, you can get:
Data Set Overview
Basic Data quality information
Data formats and data patterns
Data domains or custom data tags
Data Catalog Integration support:
Native integration support is one of the primary things to consider before choosing a Data Catalog tool. A data catalog tool may offer the following categories of native integrations:
Data Warehouses - Snowflake, Databricks, Google Big Query, Aws Redshift, Azure Synapse, etc.
BI/Visualization tools - Tableau, Looker, PowerBI, etc.,
Data lakes - Cloud storages etc.
Databases - Relational/Non Relational/Graph
Messaging/Streaming services - Kafka etc.
Data catalogs can be natively embedded within a data or analytics platform, or they can exist as standalone entities. Some standalone data catalogs are able to be embedded with an API for a more cohesive user experience.