A massive volume of data is being generated every day – intricate machines and technologies now collect an incredible amount of data – 1.145 trillion MB per day – from various sources. Finding a storage solution for storing these humongous amounts of data is of utmost importance. Most organizations dealing with multiple data sources consider using either a data lake or a data warehouse as a repository.
So what do these terms mean and can both the platforms work in synergy?
Understanding Data Lakes and Data Warehouses
Data lake architecture stores large amounts of data – structured, unstructured, and semi-structured data in its original form and provides users and developers with self-service access to siloed information. Data lakes are ideal for machine learning use cases. They provide SQL-based access to data and provide native support for programmatic distributed data processing frameworks like Apache Spark and Tensorflow through languages such as Python, Scala, Java, and more. It supports native streaming, where streams of data are processed and made available for analytics as it arrives. The data pipelines transform the data as it is received from the data stream and trigger computations required for analytics. The native streaming feature of the data lake makes them highly suitable for streaming analytics.
The primary purpose of a data lake is to make organizational data from different sources accessible to various end-users like business analysts, data engineers, data scientists, product managers, executives, etc., to leverage insights in a cost-effective manner for improved business performance.
In contrast, the data warehouse has a long history as an enterprise data technology to store structured data for specific business purposes and serve it as reporting or Business Intelligence tools. It stores current and historical data in a single place to help organizations make informed decisions on key initiatives. Data warehouses support sequential ETL operations, where data flows in a waterfall model from the raw data format to a fully transformed set, optimized for fast performance.
The data warehouse architecture relies on the structure of the data to support highly performant SQL (Structured Query Language) operations. Some newer data warehouses support semi-structured data such as JSON, Parquet, and XML files. They provide limited support and diminished performance for such data sets compared to structured data sets.
As per John Riewerts, VP of Engineering, Axiom, the data warehouse has helped us get to where we’re at today. “Data warehouses offer speed, optimization, appropriate indexing, and a fully structured data model,” he said.
Data warehouses are becoming more vital for businesses wanting to make confident decisions. Their Business Intelligence (BI) analysis teams rely on the data warehouse to provide valuable information to present to the decision-makers. Most business users access data stored within data warehouses through connected BI tools like Tableau and Looker.
Co-existence of Data Lakes and Data Warehouses
There is a common trend among businesses to narrow down the gap between these two platforms. Both the data lake and data warehouse are two sides of the same coin – they can sit together to meet both types of use cases that are mutually independent. Some industry experts believe that the gap between the data lake and data warehouse is narrowing down.
“Convergence is happening from both sides. The data warehouse vendors are gradually moving from their existing model to the convergence of the data warehouse and data lake model. Similarly, the vendors who started their journey on the data lake-side are now expanding into the data warehouse space,” said Debanjan Saha, VP, and GM of Data Analytics services, Google Cloud
Explaining how both the platforms can have worked together, John Riewerts shares, “Whether you’re building initial campaigns, and a client may have heavy data analysts who understand SQL, or you’re working with different data scientists, who understand a plethora of different technologies, their languages, their Python, R, pulling in different frameworks, like TensorFlow, you name it, tying all that together in a cohesive manner is the fun challenge. But at the root of that, in my personal opinion, this is where the data lake and the data warehouse have a nice relationship with each other.”
Ravi Achukola, Associate Vice President, Global IT Digital, Data & Analytics, Hewlett Packard Enterprise asserted that “Data lake and data warehouse have a grey line. Businesses are demanding real-time or almost near real-time analytics. Data lakes and data warehouses are best for this purpose, but when you have discrete data coming from different clouds, all you need to do is mashup in real-time, and do a fast turnaround. It is essential for us to build modern data architecture to help the customer, make their journey smooth, more effective, and turn that into an opportunity for us.”
As per Ashish Kumar, Technical Director at Qubole, the gap is going to narrow down between these two platforms. “The data lake is going to be the superset that can decipher all data warehouse problems along with more capabilities. Qubole is thinking in that direction where most of the problems we try to decipher can be solved by the data warehouse,” he said.
To sum up, in a market where leveraging data in novel ways could offer a competitive advantage, the focus should be on realizing the complementary functions of both data lake and data warehouse platforms and work towards a modern architecture that gets the best out of both.