Evaluating Data Storage Solutions as Data Lake and Data Warehouse Technologies Converge

An Introduction to Data Lakes and Data Warehouses

As businesses increasingly strive to make better data-driven decisions, they often find themselves looking for improved ways to store and access large volumes of data. One common data storage strategy involves using Data Lakes and Data Warehouses in tandem to manage raw and processed data.

Some new technologies exist that bridge the strengths of Data Lakes and Data Warehouses together in integrated solutions. These “Data Lake Houses,” provide the data structures and management features Data Warehouses offer, but for Data Lakes and can be more cost-effective for managing data storage.

In this post, we’ll evaluate the strengths and weaknesses of Data Lakes and Data Warehouses, introduce Data Lake Houses, and identify factors to consider when establishing your data storage strategy.

Data Lake

A Data Lake is a centralized location for storing, processing, and securing large amounts of structured, semi structured, and unstructured data. It can store and process data in a variety of formats, without size or volume limitations. Data Lakes are often a good place to store data until it’s ready for reporting and other uses.

Sometimes data engineers process data in a Data Lake and feed it into a data Warehouse. One example of this use scenario is storing event-level Clickstream data in a Data Lake, then aggregating weekly KPI performance at the channel level of detail for a Data Warehouse to use in reporting.

While certain risks are associated with the less structured data stored in a Lake, a solid data governance strategy will ensure that access to sensitive data is appropriately regulated (to avoid creating a data swamp).

Additional Characteristics of Data Lakes:

  • A better option for technical users such as data scientists, data engineers, and analysts
  • Raw, unstructured data
  • Schema defined after data is stored
  • Better choice for ELT Process
  • Better for storing the data you want to keep, but with immediate plan to use
  • Better for storing data intended for use in predictive analytics
  • Example: Microsoft Azure

Data Warehouse

A Data Warehouse integrates data and information from different sources on a regular schedule and stores it in one comprehensive repository of structured data. A Data Warehouse might combine customer information from an organization’s point-of-sale systems or other transaction technology, CRM, website, and customer feedback. This data is readily available for analysts to access and use to make more informed business decisions.

Risks associated with Data Warehouses include higher upfront time investments to process the data before it can be used and the added expenses of storing large amounts of data.

Additional Characteristics of Data Warehouses:

  • A better option for business-focused users as the data is more suitable for use by a variety of analysts, including BI and marketing analysts
  • Uses structured, processed data
  • Schema is defined before data is stored
  • Better choice for ETL Process
  • More immediate access to data for quicker, more efficient analysis
  • Example: Amazon Redshift

Data Lake Houses: Tools that Bridge the Gap Between Data Lakes and Data Warehouses

As new data storage technologies emerge, users can integrate their Data Lake and Data Warehouse strategies. These provide the scalability of a Data Lake and the accessibility of a Data Warehouse in one tool/product. The following bridge the two environments in a Data Lake House, which enables greater flexibility in processes and workflows:

  • Snowflake: Data Warehouse with Data Lake capabilities
    • Large storage capacity with the ability to store structured and unstructured data
  • Databricks: Data Lake with Data Warehouse capabilities
    • Includes an SQL engine for querying semi-structured and schema-less data

Key Considerations for Choosing a Data Storage Solution

  • Who will use the data?
  • Will all the data stored be used immediately? For what purpose?
  • How do you currently process data and how will this storage solution fit into that process?

How Evolytics Can Help

The Evolytics Data Engineering team brings extensive experience in managing and developing data storage strategies. We develop Data Lakes and Data Warehouses solutions, and will integrate for the flexibility of a Data Lake House environment. We’ll evaluate which solutions best serve your business needs and help you get the most out of your data storage strategy.

Whether you plan to establish a new Data Lake, Warehouse, or Lake House, need to audit your existing data strategy for recommended improvements, or want to create or improve ETL or ELT processes for your data, we’re here to partner with you.
Let’s Talk.

Written By

Kate Marxkors

Kate Marxkors is a Data Engineer who prepares and publishes data for analysis. She has experience in marketing and healthcare, unifying numerous data sources to enable meaningful reporting and analysis. Outside of work, Kate enjoys cooking, playing foosball and teaching her dog Bug new tricks.