In brief

In brief

  • Enterprise-wide data lakes are increasingly popular ways to store structured and unstructured data in one, centralized repository in the cloud.
  • A data lake implementation must be planned and executed properly, with the right methodology and accelerators.
  • An SAP data lake accelerator facilitates rapid buildout of a data lake. Companies can improve speed to market as well as time to insight.

Data lakes in an SAP environment

Sophisticated data analytics capabilities are increasingly feasible and scalable, aided by new cloud infrastructures. That’s important to SAP enterprise customers who are looking for ways to gain more value, faster, from both SAP and non-SAP application data. Many customers are looking into enterprise-wide data lakes for help, but often face significant obstacles such as complex skills requirements and configuration complications. A data lake accelerator can help companies meet the challenges of implementing data lakes rapidly and cost-effectively.

Reaching safe harbor with your SAP data lake

Most SAP enterprise customers are looking for ways to manage multiple data types coming from a wide variety of sources. Faced with massive volumes and heterogeneous types of data, organizations are finding that, to deliver insights in a timely manner, they need a data storage and analytics solution in the cloud that offers more agility and flexibility than traditional data management systems.

An enterprise-wide data lake is a new and increasingly popular way to store and analyze data that addresses many data challenges. A data lake enables you to store all of your structured and unstructured data in one, centralized repository in the cloud. Since data can be stored as-is, there is no need to convert it to a predefined schema and you no longer need to know what questions you want to ask of your data beforehand.

Where are you on your data voyage?

Many companies are stuck on shore when it comes to data. To reach their desired business destination, they need to be able to analyze vast amounts of data across silos, sometimes in real time, to fuel growth and product innovation, and deliver world-class customer service. In an SAP environment, data is sometimes trapped in SAP and many processes need to be created to get the data out. The data structure may be fractured, resulting in differing points of view on reporting requirements across functional domains.

Why use a data lake accelerator?

A data lake accelerator can support faster, lower-cost development and provide an enterprise-wide, single version of the truth for corporate reporting. An effective 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 leading practices
  • Parameterized framework
  • Automated file movement
  • Scheduled data crawling
  • Reduced deployment lifecycle
  • File-level quality controls
  • Stable reference architecture

Essential features

At their best, accelerators leverage extensive knowledge, experience and assets to help companies avoid common pitfalls. Some important features include:

  • Proven configurations
  • Accepted reference architecture
  • Deep skills
  • Embedded experience

Accelerating the journey to business value

An 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.

An SAP data lake implementation must be planned and executed using the right methodology, technical architecture and accelerators.
Subscription Center
Visit our Subscription and Preference Center Visit our Subscription and Preference Center