What is the difference between data lakes and data warehouses?
(Photo : Image by Pete Linforth from Pixabay )

Big data and analytics are crucial for business today, and the pressure on data analytics performance keeps on rising. Query response time is a competitive differentiator today, with enterprises that lead the way in data analytics able to respond faster to trends in customer demand, adjust more swiftly to changes in customer behavior, and act quickly to mitigate emerging risks.

Big data is expanding at the same time as the demand for access to analytics is increasing. Every department and stakeholder has queries, and they are coming in thick and fast. It's no longer practical to funnel all your queries through your data science team; stakeholders need to access data insights independently through self-service portals, which in turn requires systems with intuitive interfaces that are easy to master. 

Your data science team needs the tools and the time to keep developing new ways for your organization to monetize data and manage it more efficiently. In this context, it is crucial to make careful choices about the various components in your data analytics ecosystem and choose units which work well together, including understanding the nuances between data lakes and data warehouses.

What is a data warehouse?

Most people are more familiar with data warehouses than data lakes, because they've been around for longer. A data warehouse is a system that brings data in from a range of sources into a single repository, so that it can be used for data analytics. The main purpose of a data warehouse is to prepare data and make it accessible for analytics, instead of focusing on long-term storage. That's why data warehouses hold only structured data that's already been processed and is ready for analysis, and typically hold smaller datasets than data lakes.

Most of today's data warehouses have analytics and data visualization tools built into the system, as well as helping your external analytics tools crunch data effectively. When you're looking for a data warehouse, like comparing Redshift vs. Athena, built-in analytics are one of the elements to consider.

Why use a data warehouse?

With a data warehouse, you can run powerful analytics on massive datasets that would otherwise be unmanageable. Data warehouses are intended for specific use cases, namely business intelligence (BI) and reporting, and are used by business analysts. A data warehouse stores historic and relational data, such as datasets from transaction systems and operations data, bringing together data from a range of sources so it can be analyzed in a connected manner.

Once data has been processed and sent to a data warehouse, it can generally only be accessed using SQL or certain custom drivers, although some newer versions can support semi-structured data using JSON, Parquet, or XML. Data warehouses use sequential ETL to preprocess data before feeding it to BI tools.

The data is processed according to a waterfall model, so it flows through the process from raw data to fully transformed data. ETL sequential processing is optimized for fast responses to business queries, and the speed of response from data warehouses is one of their main advantages.

What is a data lake?

A data lake is a low-cost way to store data until you're ready to process and analyze it. It can hold enormous amounts of data, so there's rarely any need to purge data.

In contrast with data warehouses, the data in a data lake is usually raw data that hasn't been processed at all, although data can also be returned to a data lake after it's been aggregated with other datasets, formatted, and analyzed. Data sets can be structured or unstructured, which means data lakes can also handle unconventional data like log data or sensor data.

Why use a data lake?

Data lakes are needed for a large number of use cases, including machine learning (ML) analytics, real time analytics, and streaming analytics, and are typically used by data scientists and data analysts.

Data lakes use iterative and continuous data engineering, instead of the sequential engineering of data warehouses, and support programmatic distributed data processing frameworks like Apache Spark and Tensorflow, through languages such as Python, Scala, and Java.

Raw data streams into data lakes from every source, no matter how it's formatted, so data lakes serve as a single data repository that helps remove silos between data sources and ensure that everyone in the company is aligned by the same source of truth.

Which do you need, a data lake or a data warehouse?

Very often, enterprises need both a data lake and a data warehouse, because they serve different purposes and work together to form an efficient data analytics system. Frequently data pipelines draw data from the data lake to the data warehouse for sequential processing and BI analysis. 

If it's really a choice between one or the other, you'll need to think about your use cases. ETL sequential processing is ideal for reporting and historical data analytics, so if you're primarily looking to analyze systems data, optimize operations, and track and understand historical data, data warehouses could be your best choice. The data is already structured, cleaned, and ready for analysis, so you get faster answers.

However, data lakes are best for businesses that expect to span a number of use cases. If you want to run streaming analytics, ML analysis, predictions, and/or real time analytics, you'll need the iterative, continuous data engineering of data lakes. Data lakes are also the best bet for storing huge datasets in many formats, including unstructured data, log data, and sensor data.

Data lakes and data warehouses are part of the same system

It's important for enterprises to understand the different roles played by a data lake and a data warehouse, because at the end of the day, an effective business analytics system is likely to need them both. By ensuring that you choose wisely, you'll be able to enjoy visibility into markets, customers, and trends, and improve business decision-making with reliable insights.