“Information is the oil of the 21st century, and analytics is the combustion engine” – Peter Sondergaard, Senior Vice President, Gartner.
The expanding volume of data generation means that companies need a place to store all this data and a robust analytics engine to derive actionable insights. This is where data warehouses and data lakes come into the picture.
Data warehouses and data lakes are used by businesses for storing, managing, and analyzing vast volumes of data. Data warehouses store structured data gathered from diverse sources. The data has already been cleaned and categorized to be stored in complex tables. Businesses can use this data directly to create reports and dashboards for gaining insights.
On the other hand, data lakes are highly scalable storage repositories that hold data in its native format until it is needed to be processed. They contain a mix of structured, semi-structured, and unstructured data. Data lake offers an effective solution for companies that need to collect huge amounts of data but do not necessarily have to analyze it right away.
In this post, we’ll look at the differences between a data warehouse and a data lake, and which storage option is the right choice for your business.
Difference between Data Warehouse and Data Lake
Data types
Organizational data from CRM and ERP applications are stored on data warehouses, while data lakes house any type of data from sources like social media, web server logs, sensor data, etc. The large volumes of such type of data make it more suitable for storage on data lakes as they are scalable.
Processing
In a data warehouse, the data goes through the ETL (Extract, Transform, and Load) process in which the data is cleaned and organized before it is written and stored. This process is called ‘schema on write’. A data lake, on the other hand, consumes everything in its original format. Data is stored in raw form following the ELT process; information is saved to the schema as data is pulled from the data source, not when written to storage. This is known as a ‘schema on read’.
Storage and data retention
Before loading data onto the data warehouse, a lot of work goes into analyzing the data and its business categorization and usage. Based on this analysis, complex transformations are performed on the data to enable the extraction of relevant insights. A data warehouse is an expensive enterprise resource. To reduce storage space and increase performance, data that is deemed to be unnecessary for a particular business application is not included.
In a data lake, data retention is less complicated. The data to be loaded doesn’t have to undergo any transformations. Data lake allows the analysis of past, current, and future information. There are no storage limitations with data lakes and they can be easily scaled to petabytes.
Agility
Data warehouses are designed to answer specific business questions and the incoming data should be transformed to fit its pre-defined structure. If businesses want to retain all the data for an in-depth analysis, a data warehouse is an expensive option. Also, the effort to adopt a data warehouse to new business questions is a huge burden. A data lake, on the other hand, stores data in its raw format making it immediately accessible for analysis. A user can retrieve the information and perform data analysis on the extracted data. There is no extra developmental effort needed to get the answers to varied business questions.
Security, maturity, and usage
Data warehouses are a secure enterprise technology whereas data lakes being a newer technology, do not have the same level of security. Data lakes usually do not have the feature of working with sensitive, masked data while data warehouses work well with such masked information. The end-users for data lakes are usually data scientists and data engineers who can extract insights from the massive volumes of data. Data warehouse end-users are business professionals who only have to query the data from reporting and business intelligence tools connected to the data warehouse and do not have to worry about processing the data.
Which is approach right for your business?
The answer to this depends on your current data infrastructure and the type of data and data sources that you’re dealing with. For companies dealing with well-structured information, a data warehouse will work perfectly. If your data comes from diverse data sources like real-time sensor data, images, audio, video, or social media, then data lakes are a better choice. Opting for a data warehouse for such data sources will result in a significant data loss during transformation.
If you are working with machine learning, artificial intelligence, the Internet of Things (IoT), or predictive analytics, the data stored in raw format is essential. But if your business needs are met with reports developed using a pre-determined set of queries, then a data warehouse will suffice. Data warehouses can become expensive with growing data volumes. This could limit the amount of data stored leading to data retention issues. In such cases, data warehouses can be augmented with data lakes to accumulate the rising data volumes.
Final Thoughts
Often, organizations need both a data lake and data warehouse. Data warehouses are used for daily and operational business decisions and processes, whereas data lakes are used to harness and benefit from the raw data. For effectively leveraging big data, a hybrid approach can be recommended for businesses to operate a seamless analytics engine.
Our data experts guide businesses in designing analytics solutions that involve a synergy of data warehouse and data lake. To understand which approach is better for your business and to know more about how we can help solve your big data challenges, you can connect with us at enterprisesales@celebaltech.com
