Data Management: Choosing a Modern Data Storage Solution

As the world becomes data-driven, data management has only gotten more complex. We started this six-part series by stressing the importance of data quality. In this part, we will explore better ways to store, manage and analyze your data.

While traditional data warehousing has been a go-to decision and popular choice for decades, recently two more data storage solutions have emerged: data lakes and data lakehouses. To help your data architecture decision making process, here’s an exploration of these modern data storage solutions and how they differ from traditional data warehousing.

Data Warehouse

A data warehouse is a centralized repository where wealth management firms can store all their structured and unstructured data in an organized, integrated and historical manner. In such a setup, the traditional data warehouse extracts, cleanses, transforms, and loads (ETL) data from various sources, allowing organizations to analyze the data easily for business intelligence, reporting and data analytics.

The key feature of a data warehouse is to store pre-aggregated data in a structured format, optimized for querying and reporting, and facilitate business intelligence and decision-making processes. In other words, data warehouses are designed to handle consistently sourced, structured data from a few sources using very specific schema definitions and relational tables.

Some examples of use cases where deploying data warehouse strategy may be beneficial include:

  • Business Intelligence and Reporting
  • Customer Analytics
  • Regulatory Compliance and Data Governance

Data Lake

A data lake provides a centralized repository for all enterprise data, both structured and unstructured, without any predefined schema or hierarchy. It stores data from various sources as-is, with no need for data transformations or aggregations, thus allowing data scientists and analysts to use the data for ad hoc analysis and decision-making, including data discovery, exploration and data analytics.

In such a setup, data lakes handle unstructured data from many sources and storage systems quickly. Data lakes support storing data in their original raw format regardless of the structure, allowing for greater flexibility ​​than data warehouses and speed in easy access to all your enterprise data to gain valuable insights.

Some examples of use cases where deploying data lake strategy may be beneficial include:

  • Big Data Analytics
  • Machine Learning and AI
  • Data Science Research and Exploration

Data Lakehouse

While data warehouses and data lakes have their respective use cases, there have been healthy discussions as to which storage solution is the most effective. With the exponential rise in data creation, there is a parallel rise requiring real-time analysis and self-service analytics, as features and solutions offered by both become more integrated. For that reason, we've seen the emergence of lakehouses, which act as a bridge between the two as an SQL-based platform.

A data lakehouse combines the best of both worlds, providing a cloud-based, central, and flexible repository for both structured and unstructured data, facilitating a seamless integration of analytic tools and data processing capabilities. It adheres to the ACID principles, which ensure data consistency, transactional atomicity, and scalability.

Choosing the Right Data Architecture

All three data architectures come with their own set of benefits and drawbacks, catering to various analytical requirements and use cases. However, in today's fast-paced business environment, the lakehouse’s ability to provide the elasticity, scalability, and flexibility of a data lake, coupled with the data governance and management features of a data warehouse makes it an increasingly popular choice for companies.

Whether you opt for a data warehouse, data lake, or data lakehouse, your choice must and should depend on your firm's business requirements, available infrastructure, data analytics and processing needs and budget. Each organization must assess and, if possible, take on a proof-of-concept approach to assess each solution's merits and drawbacks carefully before selecting the one that best suits your organization.

There’s a lot more to come. Next in this series on data management, we’ll discuss ​​some of the challenges with traditional data warehouse strategies and why firms should move to modern storage solutions and architecture.

For help selecting the right data storage solution, get in touch.

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