Better Data Management Strategies: Data Lake vs. Data Warehouse

Article

May 15, 2024

Data drives key business decisions. Now more than ever, effective data management is important for the success of a company, yet many organizations still struggle with disparate data storage systems, leading to the inability to make critical business decisions based on key findings. In fact, fewer than 10% of enterprises are advanced in their insights-driven capabilities, a Forrester report found.¹

Organizations must have an effective data strategy to achieve successful business outcomes. The goal should be to consolidate all the data into one place to gain valuable insights that can drive business decisions and gain a competitive edge. Combining data sources into a data lake or data warehouse architecture enables a comprehensive view of their business, customers and market trends.

In this article, we will explain the differences between a data lake and data warehouse as well as the importance of having a data strategy to enable superior data management. Utilizing platforms such as Azure Data Lake and Microsoft Purview can provide needed solutions to organizations looking to streamline their data strategies. Our professionals have extensive experience in helping businesses implement these services to enhance and streamline performance.

Data Lake vs. Data Warehouse: What’s the Difference?

Often, there is confusion between the purpose of a data lake vs. a data warehouse. Although data lakes and data warehouses are storage systems for big data, there are vast differences between the two.

Data Lake Uses

A data lake stores raw, unstructured data from disparate sources and formats that is sent to a platform like Microsoft Azure Data Lake. These file formats can encompass video files, text files, comma-separated (CSV) files and others. Think of Microsoft Azure Data Lake as the home for multiple streams of water feeding into a lake.

Data lakes support massive parallel operations and support many analysis tools. For instance, organizations can use data lakes to query data in parallel and perform necessary operations to quickly attain insights relative to the size and volume of data. Moreover, data lakes support other analysis tools for high-impact insights and trend tracking.

Data Warehouse Uses

A data warehouse is much different than a data lake. A key difference is that a data warehouse has a pre-defined reason for storage whereas a data lake does not. Additionally, data is processed for querying with a data warehouse, which is not the case for a data lake where the data is raw and unprocessed. A data warehouse is used for business analysis, whereas a data lake is often used for data analytics or data scientist analysis.

Azure Data Lake

Azure Data Lake is a cloud-based platform as a service (PaaS) solution that can manage massive data storage — trillions of files greater than a petabyte in size. This flexible and scalable platform allows organizations to store as much data into that data lake as they want without having to worry about artificial constraints.

Azure Data Lake also offers a comprehensive set of features that enables developers, data scientists and analysts to effortlessly store data of any size, shape and velocity, and conduct diverse processing and analytics operations across multiple platforms and coding languages. This eliminates the challenges associated with data ingestion and storage, streamlining the process of implementing batch, streaming and interactive analytics. Additionally, Azure Data Lake seamlessly integrates with existing IT systems for simplified data management and governance, including identity, management and security.

Lastly, Azure Data Lake also supports blob storage, which is short for binary large object. Blob storage is a type of cloud storage for unstructured data. Blob storage provides scalable, cost-efficient storage of unstructured data in the cloud and mobile applications. Azure Data Lake supports data in its native form.

Microsoft Purview

Let’s now connect the dots between Azure Data Lake and Microsoft Purview. Remember, Azure Data Lake is the storage of your data. Microsoft Purview (previously Azure Purview) provides an improved security connection to your data lake ingestion, storage and analytics pipelines to automatically catalog data assets. Microsoft Purview connects natively with Power BI — a collection of software services and apps — and other reporting and visualization tools, showing the lineage of data used in end reports. It also shares sensitivity information from the Power BI assets to prevent incorrect data use.

Below are some examples of how to use data. Effective data storage leads to improved accessibility which generates better outcomes.

Capabilities of Purview include:

Catalog: Automatically capture and describe core characteristics of data at the source.
Classification: Classify datasets and data elements with predefined sensitive-data classification.
Access Control: Define and grant access to data assets and glossary items in the catalog.
Insight: Provide multiple predefined reports to help data professionals gain a detailed understanding of the data landscape.

Microsoft Purview provides a unified data governance solution to help manage and govern your on-premises, multi-cloud and software as a service (SaaS) data. Well-managed data is easily accessible and searchable across the organization while maintaining security, ensuring data quality and reliability.

Business Outcomes

Your data is now stored properly. Now what? Here are a few ways an effective data strategy can improve your organization:

Trust: Good data governance builds trust in your data environment. Knowing what the source of truth is for your data reduces risk of compliance or regulatory issues.
Make Informed Decisions: Good data provides indisputable evidence for ways to streamline processes, save time or get a better return on your investments.
Measure Effectiveness: Data helps you measure the effectiveness of a business strategy and determine how well your solution is performing in the marketplace or even internally with employees.
Advocate New Processes: Using data will present a strong argument for systems change, whether you are advocating for increased funding for a project or making the case for a system upgrade.
Know What Is Working: Data allows organizations to replicate areas of strengths and will support high-performing programs or service areas.
Stretch Your Dollar: Data will help organizations make sense as to where to spend money and where to cut back.

Connect with Cherry Bekaert

As a Microsoft Partner, Cherry Bekaert offers a range of Microsoft solutions to optimize your business whether that’s implementing critical platforms or training on how to maximize the software. We can help you get started on your journey to better store and manage your data. Our team of professionals have broad industry experience and keen business acumen. We look forward to hearing from you.

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