Data lake or data swamp?
Data keeps gushing in at high speed. So, are you creating a data lake from it, or a data swamp? We all know that sophisticated data analytics capabilities are becoming more and more prevalent and scalable, supported by new cloud infrastructures. That’s important to my SAP enterprise customers who are looking for ways to gain more value, faster, from both SAP and non-SAP application data. Many of those customers are looking into enterprise-wide data lakes for help, but I’m finding that they encounter significant obstacles such as configuration complications and the need for new, complex skills.
To help companies meet the challenges of implementing data lakes rapidly and cost-effectively, Accenture and AWS have created an SAP Data Lake Accelerator that is based on leading practices and offers faster deployment at less cost.
Features of the SAP Data Lake Accelerator from Accenture and AWS
The SAP Data Lake Accelerator helps minimize time and effort required to set up, configure and maintain a data lake by providing a variety of data ingestion and basic curation methods in a well-defined zonal and secure architecture. This makes possible fully automated movement of data using AWS native services.
The accelerator includes:
- Automated analysis of SAP source systems and automated set-up of the data extraction
- Automated generation of SAP data extraction
- Automated zone creation based on best practices
- Parameterized framework
- Automated file movement
- Scheduled data crawling
- Reduced deployment lifecycle
- File-level quality controls
- Stable reference architecture
Building on knowledge and experience
A distinct way that the accelerator is different is that it leverages extensive knowledge and assets from both Accenture and AWS to help companies create and manage their data lakes effectively:
- Proven configurations: Setting up and configuring a data lake can be time consuming. It is difficult to build and extend complicated curation logic. Because the data lake has no native data quality service, basic data checks are required to ensure that data is trustworthy. There is also no native lineage service, so tracing the lineage of data is a challenge.
- Accepted reference architecture: Data lakes are new, so there is not yet a market-agreed reference architecture—indeed, there are thousands of permutations. Companies need a proven, out-of-the-box architecture to speed the creation of their data lake.
- Deep skills: Broad SAP aptitude is required, including functional, business warehouse, and interface development skills. An SAP interdisciplinary team must know how to work in a complementary way with AWS technical architects. AWS has many services available that comprise a data lake solution, and the knowledge within the accelerator helps companies architect and integrate those services.
- Extensive experience: Deep experience is needed to expedite a data lake implementation—across analysis, design, build and deploy. Many challenging questions are involved: What are the pros and cons of different extraction mechanisms? What data is relevant in the source systems?
Accelerating the journey to business value
The SAP Data Lake Accelerator can deliver multiple benefits before and during a data lake implementation in an SAP environment. Companies can fast-track their data lake buildout, so they can improve speed to market as well as time to insight. They can also embed more flexible architectures into the data lake.
At the scaling phase, the data lake supports better data-led decision-making because the data is more trustworthy. The data lake also supports on-demand data consumption while providing strong governance of data access and delivery. Faster innovation is made possible by enabling ready analysis of many varied forms of data. The accelerator helps companies drive business change quickly in an agile fashion for a low cost of entry.
Get in touch
For more information about the SAP Data Lake Accelerator from Accenture and AWS, please write to: SAPDataLakeAWS@accenture.com