Data lakes have, in today’s highly competitive data-driven business environment, emerged as an optimized platform for data storage and computing capabilities. One of them is the SAP data lake but before diving into its functioning and intricacies, it is necessary to understand the concept of data lakes and how they have taken the working of organizations around the world to the next level.

A data lake is a perfect repository for all types of data in its native format, from unstructured and semi-structured, to structured data. This data can be accessed at any time for analytics and arriving at critical business decisions without the need for formatting or processing it. If you incorporate a technologically advanced data lake with cutting-edge features like the SAP data lake in your IT setup, you can reap multiple benefits like lower costs, quick and seamless access to data, and improved database performance.

A clarification about the distinction between a data lake and a data warehouse is required here because people generally talk about the two as if one can be substituted by the other. In reality, there is a world of difference. While data in its raw form can be stored in a data lake, only cleaned, structured, and formatted data can be stored in a data warehouse.

Further, the generic architectures and the designs of data lakes are not standardized and vary across platforms. Hence, though SAP data lake and Snowflake are both data lakes, the structure of the two are far from similar. Which one is used by an organization depends upon specific operational requirements.

The Evolution of the SAP HANA Data Lake 

In April 2020, SAP launched the HANA Data Lake (HDL) that further added to and strengthened its cloud-based ecosystem. The purpose was to provide customers with a highly optimized and cost-effective storage system. The bundle of benefits came with a native storage extension and an in-built relational SAP data lake. It put SAP data lake in the same league as Microsoft Azure and Amazon S3 (Simple Storage Service) because of its advanced data processing capabilities and functionalities.

One of them is the 10x data compression feature. It results in huge data storage savings as massive volumes of data can be significantly reduced in volume before storage, leading to fewer resources being used. Also, SAP offers the option of either keeping the SAP data lake in the current HANA Cloud or a new HANA Cloud instance. In both cases, users can add storage space any time on demand and get the benefit of all cloud-based data lake features like tracking data access, data encryption, and audit logging.

The Structure of the SAP Data Lake

The SAP data lake has a unique architecture that is not seen in other data lakes. Here, businesses can store their frequently-used and most critical data (hot data) for quick and ready access while moving less-used data (warm data) to the SAP HANA Native Storage Extension (NSE).

Here is a detailed look at the SAP data lake architecture.

Think of the SAP data lake as a pyramid, divided into three segments.

At the top is the hot data as explained which is very valuable for businesses and constantly used. Therefore, the cost of storage of this type of data is highest on the SAP data lake and is frequently accessed and processed for analytics.

The middle of the pyramid holds data that is not often used but has enough significance not to be deleted from the data lake. The data (warm data) is not very critical for organizations and not as high performing as that in the top tier. Access requirement to this layer is quite slow.

At the bottom of the pyramid is rarely-used data that in older and traditional databases would have been deleted to save precious resources. But not so in an SAP data lake where this data can be stored at rock-bottom rates. The trade-off in this section is that the data access is very slow.

The advantage of this tiering is that businesses get to store data at significantly lower costs because storage costs depend proportionately on the volumes in each category unlike flat fees in traditional data lakes. Support is also provided to data during its full life-cycle, starting from hot to warm to cold data.

Advanced Features of SAP Data Lake

An SAP data lake provides users with several advanced features that are inherent in a cloud-based environment. Here are a few of them.

The SAP data lake works independently of HANA DB and is based on SAP IQ technology. It has flexible and easily scalable storage capabilities and can quickly provide petabytes of storage space whenever required. Hence, for a sudden spike in demand for additional resources, businesses do not have to invest heavily in hardware and software to meet this requirement.

Since SAP data lake operates in the cloud, users get seamless connectivity and link to other high-performing cloud providers like Amazon Web Service Simple Storage Service and Google Cloud Platform Cloud Storage.

Users of SAP data lake get all the features that are inherent in the cloud including automatic provisioning and high-performing data analysis. These are matched and administered with the HANA Cloud along with maximized speed ingestion.

The SAP data lake has a very low Total Cost of Ownership (TCO) which is a financial benchmark for storage costs.

Those having on-premises SAP HANA can choose to be on the cloud-based platform by selecting HANA Cloud as a hybrid option, thereby availing the full benefit of the affordability of SAP data lake.