How a data lake differs from a data warehouse has evolved over the last few years. Read on to see how the two data repositories differ in 2021.
Data is growing exponentially in all fields, yet still many organizations struggle to turn all of that data into value. Deciding how to store and manage all of your data is a critical decision that will enable your organization to become truly data-driven in the most cost-effective way.
In the recent past, you couldn't talk about being data-driven without talking about data warehouses. Now, as a result of big data, you can't talk about being truly data-driven without at least considering data lakes to either complement or replace data warehouses.
In this post, we’ll take a look at five key factors to consider when deciding between data lakes and data warehouses. You’ll also see why the best approach may involve a strategic use of both data lakes and data warehouses as part of a more comprehensive data management strategy.
But first, let’s start with the basics.
What is a data warehouse?
A data warehouse is a tried and proven method for turning data into intelligence that has been around for decades. Data must undergo ETL (Extract, transform, load) before getting stored in a data warehouse. By keeping only processed data, data warehouses ensure no storage space is wasted and that all data is optimized for business intelligence and analytics.
However, with the rise of big data and new data sources (e.g., sensors and social networks), the highly structured nature of a warehouse is considered an obstacle to the agility and flexibility needed to be competitive in this fast-paced era. Additionally, data warehouses can’t cost-effectively store the extremely large data volumes that are coming in from so many sources.
For this reason, data lakes have become popular.
What is a data lake?
A data lake is an emerging storage repository trend that has been gaining recognition beyond data storage for its ability to deliver business value. A data lake stores all types of raw data in its native format, from structured spreadsheet data to unstructured social media activity data. Additionally, it doesn’t require a time-consuming ETL process before loading and storing, which means that huge volumes of data can more quickly and cost-effectively be stored in data lakes.
Data Lake vs. Data Warehouse: Which approach should you choose?
In recent years, the new demands brought forth by big data have led to the rise of data lakes. Data warehouses, on the other hand, have been a proven solution for decades. So which should you choose?
Here are five key factors to consider in 2021.
1) Raw data vs. processed data
A data lake stores any data in its raw form—structured, semi-structured, and unstructured— whereas traditional data warehouses predominantly store structured data. Organizations primarily use data lakes to store data from newer sources, such as IoT sensors, social media posts, chat text, and more. Warehouses are technically able to store data from these sources as well, it’s just not as efficient or cost-effective.
It’s not uncommon for data that’s loaded and stored in a data lake to not have a known purpose yet. Actually, data that gets stored in a data lake may never end up helping your business, but because it might, it’s worth storing.
On the other hand, data loaded onto a data warehouse is processed with a specific purpose in mind. In fact, it was common to throw away data unless it had a known business case. Now, instead of turning this data away, businesses can store that data in a data lake where it might prove valuable in various ways in the future.
You should consider a data lake if you are ready to leverage big data from various sources. It’s more cost-effective and efficient than data warehouses at dealing with the large influxes of data caused by the explosion of new data sources in the 21st century.
For decades, if businesses wanted to equip their frontline workers (e.g., salespeople, marketers) with data at their fingertips, data warehouses have been the clear go-to. However, with the quality of data lakes significantly improving in recent years, you now have to be more specific about the type of analytics you need when determining the best solution for you.
When data lakes were newer, data lake initiatives struggled with a lack of data governance. Data lakes quickly became what is called data swamps—a metaphor for a data lake that is full of bad-quality data. Essentially, data lakes were more known for being a cost-effective storage option than they were for providing rich analytics.
Fast forward a few years, and businesses now have many tools to ensure their data lakes stay clean. For example, machine learning algorithms can quickly go through high volumes of data in minutes to deduplicate and merge data, identify schemas, and much more. As a result, it’s now much easier than before to gain rich business insights from data lakes.
Data lakes now also provide a great source of data to power special types of analytics. For example, because data lakes don’t require a resource-intensive ETL process, data lakes excel at powering real-time data analytics. It’s also a great source for predictive analytics.
On the other hand, you should still consider data warehouses to equip your workforce with business intelligence reporting, batch reporting, and other pre-defined reports to power strategic decisions.
3) Artificial Intelligence and Machine Learning
Artificial intelligence and machine learning have proven to be very valuable in data lakes in multiple ways.
New data sources today provide a tremendous amount of unstructured data, which can easily get overwhelming. Artificial intelligence and machine learning can quickly go through massive volumes of unstructured and semi-structured data to help clean and prep the data.
Then you can use data to train machine learning algorithms and build better models. Basically, machine learning helps prepare data for machine learning-powered analytics (e.g., predictive analytics).
Analyze data more holistically with data lakes and machine learning
Data lakes allow you to look at your data more holistically by breaking down data silos in your organization. Traditionally, you would hold social media data separately from structured CRM data stored in data warehouses. Although you can still get valuable insights this way, you unlock richer insights by using machine learning to analyze the data as a whole instead of in silos.
Additionally, after data has undergone transformation in the data warehouse, you can load that data back into the data lake. This way, you can analyze the structured data with the rest of your business’s unstructured data. Essentially, this allows you to find trends between sales reports (structured data), social media comments, and website activity (unstructured data).
Warehouses offer limited functionality for machine learning. Machine learning and artificial intelligence thrive in the type of environment that data lakes provide. Data lakes can process image data, audio data, and all types of raw data to feed machine learning models and run experiments.
A key advantage of data warehouses over data lakes that’s often cited is that non-data scientists more easily understand the data in warehouses. However, that gap has been reduced significantly in the past few years because newer technologies and integrations have enabled self-service and smarter data lakes.
Additionally, machine learning can perform the heavy lifting of cleaning and transforming data so that it is more readily available for analytics by non data-scientists.
Although data warehouses are still more user-friendly to business users and non-data scientists, the advancements in technology closing this gap are happening fast and it's something to look out for.
Data lakes store large volumes of data more cost-effectively than data warehouses do.
For data to get stored in warehouses, it must undergo the ETL process, which can be costly and time-consuming. Data that gets stored in data lakes doesn't require ETL before storage. By decoupling storage from compute, data lakes can load and store an increasing amount of data very quickly and cost-effectively, making it a perfect solution to accommodate the storage demands of big data.
Although data lakes require a larger storage capacity, it's still more cost-effective than data warehouses because you can scale storage independently from compute. Data lakes also allow you to tier infrequently accessed data to cheaper storage.
The future of data lakes and data warehouses
Data lakes and data warehouses both are critical parts of a future-proof data management strategy. And as we move into the future, data lakes will continue to add warehouse-like features, while data warehouses will add lake-like features to close the gap on their differences.
So much so, a new concept called a data lakehouse has risen, intending to merge the benefits of both a data lake and data warehouse.
Data warehouses and data lakes will bring more features that aim to democratize data and enable data-driven decisions for all types of users, not just data analysts. However, one key challenge will remain: balancing the democratization of data while maintaining compliance with regulatory requirements.
To learn more about data lakes, check out this step-by-step walkthrough on how the AI company Cloud Brigade used a data lake and external data from John Hopkins to automate the creation of dashboards.