If you use a data lake, chances are you need a way to keep your data searchable for business users. When combined with the analytics capabilities of a cloud data platform, a data catalog can solve some of the common pain points around “data swamps,” where users fail to gain any meaningful insights from their data.
Some of a business’s most valuable assets lie within its data. At a high level, you can think of a cloud data catalog as an inventory of all of your data assets, improving your team’s access to data and data discovery capabilities. In other words, if you want to get more organized in 2023, a data catalog might be for you.
In the past, many teams have been frustrated with self-service business intelligence (BI) tools because their data and analytics technology behind the scenes wasn't built to support this style of quick search and recall. In reality, data scientists and data engineers have to act as data stewards, providing the data governance layer that ensures information is both available and trustworthy.
Let’s learn more about data catalogs and dive into some challenges of a data lake architecture. We’ll cover how organizations can solve these challenges with a combination of data catalogs and a cloud data platform for analytics like ChaosSearch.
What is a Data Catalog?
At its core, a data catalog is essentially an organized database that stores information about all of your company’s data assets. It stores detailed metadata about each asset—including who created it, when it was created, and where it was stored—in an easy-to-navigate format. This allows users to quickly find and search for any information they need without getting lost in a tangled web of folders or file pathways. In addition, it can also store descriptions about each asset so that anyone looking for specific information can quickly see if it exists in the database before investing time in further research.
Why Do You Need a Data Catalog?
Enterprise data catalogs help businesses make sense of their vast stores of data by providing an organized view of every asset within their system. This makes it easier for users to locate specific information quickly and efficiently while also freeing up resources that were previously wasted on manual searches. In addition, it also helps improve accuracy by ensuring that only verified assets are available for use.
For example, with a data catalog in place, you can avoid accidentally using outdated or incomplete information because each asset will include verification dates. Lastly, having access to comprehensive metadata about each asset helps improve security by ensuring only authorized personnel have access to sensitive information within your system.
Check out the Case Study: Equifax Simplifies Cloud Operations Management Globally with Cloud Data Platform
The Advantages of Using Data Catalogs
Using a data catalog provides numerous advantages for businesses looking to maximize their use of cloud data platforms and data lakes. Not only does having an easily accessible database make finding relevant data much easier, but it also reduces the amount of manual work required by staff members searching through records or manually verifying assets before use.
For example, without a data catalog, a business analyst searching for “2022 customer profitability” information may find many spreadsheets that could potentially meet their needs. They may need to spend time sifting through these spreadsheets to understand what they’re all about. A data catalog could save time by defining the data artifact and its relationship to other data artifacts throughout the company.
Having this level of organization also greatly improves security since only authorized personnel will be able to access sensitive data or documents stored within your system. Finally, using comprehensive business metadata ensures that everyone has access to accurate and up-to-date information when working with digital assets within your network.
Read: Business Intelligence on the Cloud Data Platform: Approaches to Schemas
Common Data Management Challenges of a Data Lake
Data lake infrastructure presents many unique data management challenges. In contrast to traditional databases and data warehouses, data lakes rely on vast amounts of unstructured and semi-structured data that require extra measures for proper organization and control. To keep a data lake running smoothly, companies must be proactive when it comes to curating and organizing their vast amounts of information.
The first challenge with data lakes is scalability. A large amount of incoming data needs to be stored and managed without overwhelming the system or creating bottlenecks in productivity. Many organizations leverage low-cost, flexible cloud object storage like Amazon S3 or Google Cloud Storage. However, it’s important to note that data lake storage solutions don’t inherently include analytic features. Data lakes are often combined with other cloud-based services and downstream software tools to deliver data indexing, transformation, querying, and analytics functionality.
For example, in a data lake architecture, a self-service data analytics engine (such as ChaosSearch) sits on top of a cloud-based data repository, delivering features that help organizations realize the full value of their data. This approach can activate low-cost cloud object storage, enabling teams to ingest, index and analyze their data without having to move it into a separate ETL pipeline for analysis.
In addition, teams must consider governance controls when managing a data lake environment. Data lakes will inevitably generate petabytes of unstructured and semi-structured data over time – but how will this information be tracked? Data catalog tools allow companies to define metadata schemas and document searchable business glossaries across the entire dataset while also providing quality assurance methods to audit the accuracy of that metadata over time. Such tools are essential for ensuring that sensitive information remains organized, consistent, secure and compliant with industry standards or internal regulations.
Managing a successful data lake requires organizations not only to leverage existing technology solutions but also actively engage in curation processes, among many other activities associated with the upkeep of an efficient system that scales without compromising performance or security requirements.
Using a Data Catalog with a Cloud Data Platform
While data catalogs can certainly keep you more organized, teams still need a way to quickly analyze the data within their data warehouse without the pain of data movement. To complement your use of a data catalog, an index-driven cloud data platform like ChaosSearch can support log analytics and BI (or SQL analytics) by using a compressed index to transform, query, and search a common data store like Amazon S3.
ChaosSearch’s cloud data platform helps organizations scale their log analytics and BI workloads without incurring expensive compute cycles. It does so by making it much simpler to transform, query, and search data objects for multiple analytics use cases — ranging from supporting a best-of-breed observability approach to security operations and threat-hunting use cases.
ChaosSearch integrates with popular analytics tools, as well as feeds log alerts to incident management and collaboration tools. The ChaosSearch platform’s open architecture supports open data formats and offers governance elements such as role-based access control (RBAC). ChaosSearch supports log analytics use cases, augmenting solutions like Elasticsearch, Datadog and Splunk when customers reach the TBs per day scale level. It also supports SQL queries, which helps data analysts process more data for faster time to insights.
Read: IT Professionals Reveal Cloud Data Platform Highs and Lows of 2021
Getting More Value from your Data Lake
Data lakes present many unique challenges for companies in terms of scalability and governance controls. To manage these challenges successfully, organizations must be proactive in curating and organizing their vast amounts of information with tools like data catalogs. In addition, solutions like ChaosSearch's index-driven cloud platform can provide simple, fast and low-cost analytics capabilities on top of existing cloud object storage.
While data catalogs help businesses make sense of their vast stores of data by providing an organized view into every asset within their system, ChaosSearch makes it easier to interrogate data in many novel ways. When used together, these solutions can improve accuracy, security, and productivity for data-driven teams across the organization.
Additional Resources
Read the Blog: The Power of ChaosSearch Alerts
Watch the Webinar: Advanced Analytics - Data Architecture Best Practices for Advanced Analytics
Check out the Whitepaper: DevOps Forensic Files: Using Log Analytics to Increase Efficiency
