Modern-day businesses are fully data-driven, needing immense powers of computing and voluminous storage capabilities to function at peak operational efficiencies. This requirement is fulfilled by data lakes, a concept that is quite distinct from the traditional warehouses. Before going to the various aspects of the SAP data lake, an explanation of the intricacies of data lakes per se will be in order.
What is a Data Lake?
A data lake is a storage repository where data in any form and structure – unstructured, semi-structured, or structured – can be stored in its native formats. This data can be seamlessly and quickly accessed and processed by businesses for analytics and to make critical operational decisions. While this is just the basic outline of a data lake, an advanced one like the SAP data lake is capable of much more. This is why, by implementing data lakes into the existing IT infrastructure, organizations get improved performance and quick access to data, all at very cost-effective rates.
Even though people tend to club data lakes and data warehouses and think that one can be substituted by another, it is not so. While a data warehouse will only store data that has been cleaned and processed, a data lake can store data in its native raw and unformatted structure. Further, the architecture of a data lake is not strictly defined and it can be used as per the specific requirements of an organization.
For example, the structure of Snowflake which is a cloud-based data lake is different from that of the SAP data lake even though both have several cutting-edge and advanced features.
The Launch of the SAP HANA Data Lake
HANA Data Lake was launched by SAP in April 2020 and added more strength and robustness to the existing cloud-based business environment. The goal was to provide customers with a highly advanced yet cost-effective storage system. The complete package was created with a native storage extension and a relational SAP data lake available out of the box. This brought the SAP data lake at par with other established players like Amazon S3 (Simple Storage Service) and Microsoft Azure concerning functionalities and data processing competencies.
Several innovative features are available with the SAP data lake which will be discussed later in this post. But the most critical of them is the 10x data compression. It leads to huge savings in storage costs as the volume of data is considerably reduced through compression before storage. Further, users have the option of keeping the SAP data lake in the existing HANA Cloud or moving to a new instance. Regardless of the method opted for, it is possible to add storage space whenever needed and get all the benefits of a cloud-based ecosystem like data encryption, audit logging, and tracking data access.
The SAP Data Lake Architecture
The SAP data lake architecture is unique in its class and quite unlike other data lakes. Businesses have the choice of storing data that is frequently used and require regular access (hot data) while moving data that is not used much (warm data) to the Native Storage Extension (NSE) of SAP HANA.
The SAP data lake architecture may be visualized as a pyramid that is divided into three layers.
As explained, the top of the pyramid contains data that is regularly accessed and is critical for the functioning of an organization. The cost of data storage here is the highest of the three as the data is frequently accessed for analytics.
The second layer in the middle of the pyramid stores data that is not as critical but not insignificant enough to be deleted from the system. This is warm data that is not as high-performing as the top tier and comes with low access rates. The cost of data storage is lower than the layer before.
At the bottom of the pyramid lies data that is rarely used and would have been deleted in traditional databases. But the SAP data lake structure offers rock-bottom prices for data storage here and companies prefer to hold on to the data. The flip side is that the speed of access to this data is very slow.
The architecture of the SAP data lake facilitates storage of data at overall lesser rates than traditional data lakes where flat fees are charged irrespective of the type of data. But in an SAP data lake, there is the possibility of storing data proportionately to its importance and paying accordingly. Hence, data can be stored through its full life cycle from hot to warm to cold data.
Cutting-Edge Features of the SAP data lake
SAP data lake offers several features that are closely linked with any cloud environment. Some of the main ones are as follows.
The SAP data lake is based on the SAP IQ technology and is not linked to HANA DB. It offers flexible storage facilities on demand and users can quickly scale up to petabytes of storage space if needed. Hence, businesses do not have to invest heavily in hardware and software if there is a sudden surge for storage space.
Further, since it operates in the cloud ecosystem, users of the SAP data lake get seamless connectivity to other high-performing cloud service providers. Among them are Google Cloud Platform Cloud Storage and the Simple Storage Service (S3) of Amazon Web Service (AWS).
Finally, SAP data lake provides users with all the benefits of the cloud including high-performance data analysis and automatic provisioning. These are complemented and matched with the HANA Cloud for high-speed ingestion.