What Are Data Catalog Connectors?

Data catalog connectors are essential tools for helping organizations get the most value out of their data catalogs. Learn why they’re important here.
Last updated
May 2, 2024

Data catalog connectors are essential tools for helping organizations get the most value out of their data catalogs. These connectors can connect data sources to the catalog and help organizations eliminate their data silos. They also help with orchestration, automation, analysis and so much more. In this blog, we’ll be taking a look at data catalog connectors and why they’re so essential to integrate into the modern data stack. Read on to learn more.

A Brief Introduction

Put simply, data catalog connectors are necessary for modern data management. As a bridge between your data sources and your centralized repository, known as your data catalog, they enable data discovery and access. These connectors allow organizations to get a more holistic view of their data assets and ensure data is as accurate as possible across teams. So, we know data catalog connectors are important, but what exactly falls into the connector catalog? Let’s take a look.

Types of Data Catalog Connectors

There are numerous different types of data catalog connectors, and data-driven organizations likely already have many of these connectors already implemented into their processes. Some of the most common connectors include data warehouses, data lakes, ELT/ETL tools, orchestration tools and more. Here are some definitions for the common types of data catalog connectors you may already have implemented or are considering implementing:

  • Data warehouse — A data warehouse is a central repository that brings together all of an organization’s data. It is integral to data analytics, making the data readily available for analysis. Data warehouses may be on-premises or cloud-based.
  • Data lakes — A data lake is a form of data repository where data is stored in a raw and native format. Data lakes may store unstructured, structured and semi-structured data that is ready to be processed and ingested for data discovery and analysis.
  • Database — A database is a data repository that is used to organize and store structured data.
  • ETL/ELT tools — ETL/ELT tools are extract, transform and load or extract, load transform tools. Though ETL and ELT differ in the order they process data, both types of tools are used to extract data from data sources, transform it to a desired format and load it into a destination system.
  • Orchestration tools — Data teams and data-driven organizations use orchestration tools to run and automate data workflows. They can help manage data pipelines and make complex data management tasks simpler.
  • Data quality tools — Data quality tools are used to help monitor and maintain data quality. They are used to ensure the completeness, accuracy and consistency of data. Data quality tools may perform tasks such as data cleansing or validation.
  • Business intelligence tools — Business intelligence (BI) tools help with data visualization and data analysis, allowing business users to easily put together reports and dashboards to help make data-driven decisions.
  • Collaboration tools — Collaboration tools are used to facilitate teamwork and communication, often including features for sharing and collaborating on data and insights.

What Are the Benefits?

Data catalog connectors offer a wide range of benefits for data management processes. Here are some of the primary benefits:

  • Improved data discovery — Data catalog connectors enable more intuitive and efficient data discovery. Connecting various data sources into your data catalog allows you to get additional metadata context and create a single source of truth for your data assets. This makes your data more searchable, improving data discovery and empowering users to make more data-driven decisions.
  • Enhanced data governance -Data catalog connectors make it easier to implement data governance policies and adhere to compliance regulations. When your data assets are located across disparate systems, it can be more difficult to trace data lineage, keep data secure and maintain data quality standards.
  • Improved data accuracy — When your data isn’t siloed, and you designate data stewards across your organization, you can ensure more accurate and reliable data. As mentioned, centralizing your data into a data catalog will create a single source of truth. Meaning your data will have fewer inaccuracies, errors and duplicates.
  • Increased collaboration and efficiency — Connecting your data sources to a data catalog and improving access leads to easier and more efficient collaboration among teams and departments. A centralized catalog will make data sharing much simpler and lead to more productive teams that can seamlessly work together when needed.

How To Implement Data Catalog Connectors

If the benefits have convinced you to implement more data catalog connectors in your organization, here are some steps to help you successfully implement them:

  • Identify your data sources — First, identify the various data sources across your organization. Evaluate which ones you will need to connect and which will be sunsetted as you move forward with a new interconnected data catalog.
  • Choose your connectors — Next, you will need to choose the data catalog connectors you need to implement. Make sure to research and evaluate factors like ease of integration, use cases, scalability, budget and other important considerations when making the call.
  • Implement — Install and integrate your various data catalog connectors with your data management system. This typically involves integrating the connectors with the data sources you identified and configuring them.
  • Populate the catalog — Use the data catalog connectors to extract metadata from your data sources and populate the catalog with relevant information such as data schema, data lineage and data quality metrics. Ensure that the metadata remains up to date by regularly syncing with the data sources.
  • Implement data governance policies — Define and enforce data governance policies. Establish data access controls and data quality standards across teams and communicate them with the data stewards.
  • Train and educate users — Provide training and education to users on how to effectively use the data catalog and leverage the connectors. This will promote adoption and ensure that users fully understand the capabilities and benefits of the data catalog connectors.

Future Trends To Watch Out For

As the field of data management continues to evolve, there are several exciting future trends to monitor. These include:

  • AI — Artificial intelligence and machine learning are gradually becoming mainstream technologies in data management. These technologies can be leveraged to automate tasks, enable more powerful data search and discovery and much more. AI is evolving rapidly and will only continue to do so in the coming years.
  • Cloud-based platforms — Cloud-based data catalogs are becoming much more popular for everyone, from small businesses to major enterprises. The cloud is more usable, affordable and secure than ever, making it a preferable choice to on-premises architecture for many organizations. Cloud-based data catalogs and connectors allow for more scalability, flexibility and data accessibility. Expect more and more organizations to adopt cloud-based data management technologies in the near future.
  • Evolving data regulations — As data technology evolves, so will the regulations on how it is collected and used. Organizations need to stay on top of the latest compliance measures and regulations to avoid fines, penalties and loss of reputation.
  • Democratization — Finally, data catalogs are making it easier for users across an organization to utilize and access data. Meaning nontechnical users are empowered to utilize data without having to rely on the data team, and data teams have simpler and more efficient processes for managing data.

Try Secoda for Free

If you’re looking for the ultimate data catalog solution, Secoda is your answer. Secoda is the first AI-powered data search, cataloging, lineage and documentation platform to double your data team’s efficiency. You can leverage Secoda’s numerous data management tools to make the most of your data and maximize its potential. Learn more about Secoda and book a free demo today.

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