By Tara Callaghan, Product Manager at Datavard 
By Mattia Molteni, Solution Engineer at Datavard
By Kenny Rajan, Solution Architect at AWS

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Today’s enterprises are dealing with more than structured data that’s being exponentially generated. Customers are looking for effective ways to capture, store, curate, and analyze structured data, as well as semi-structured and unstructured data.

They are looking ways to unify real-time streaming data generated by internet-powered devices, enterprise resource planning (ERP) systems like SAP, chat logs, social networking, and application logs using scalable and highly available data and analytics platforms to enable data-driven culture.

While traditional data warehouses are likely to continue to be part of a customer’s data strategy, they do not provide a complete data platform due to scale limitations, functional capabilities, and high costs. As the amount of data accumulates, customers typically store it in siloed systems, making it difficult to aggregate the data and benefit from a full end-to-end analytics capability.

Customers understand data does not always tell them what action to take unless they use the right set of tools and strategies to unify the data, interpret it, and make good decisions. However, getting enterprise data sets into a platform and managing data from mission-critical systems like SAP can be challenging.

In this post, we will explore how Datavard Glue’s extraction capabilities help organizations enable a data-driven culture. Datavard Glue provides integration technology to build a highly scalable, available, secure, and flexible data lake powered by Amazon Web Services (AWS).

Datavard and AWS have been partnering together since 2015. Datavard Glue helps businesses drive value from their SAP data by enabling delta extraction capabilities from and to various data sources.

Data Warehousing and Business Reporting Challenges

Data Silos

Data is often held in multiple products or lines of business and module-oriented systems with no single trusted source. Many Datavard customers understand their ability to compete is increasingly driven by how well they can leverage data, apply analytics, and implement innovative technologies.

However, turning an organization into a data-informed business is an idea many companies have found difficult to pull off in practice.

Connecting data sets from disparate systems to other platforms is a challenging task for many organizations. They need to find an ERP integration technology compatible with other technologies, which often requires expert resources and complex projects to achieve the desired outcome of unifying the data. In addition, managing all of these systems can cause a lot of operational and administrative overhead.

Fractured Business Analytics

This siloed approach leads to fractured business analytics. Suppose every line of business has historically built their own analytics to serve their own needs; there is rarely consideration given to any up or downstream activities in other business units or processes.

Many organizations fail to consider how combined analytics from their systems could benefit the business in general.

As a result, they’re often deciding on their own tools, best practices, and standards. This makes any attempt at reconciliation or aggregation a significant challenge. In the end, there is often no single source of truth for insights or intelligence, and certainly no connection to relevant, rich non-SAP data sources.

Usability of Data

Today’s real-time, real-world data usually comes from multiple, disparate sources—IoT devices, messaging applications, social media, clickstreams from web, and ecommerce activity, for instance. It’s inherently challenging to identify, understand, access, extract, and unify disparate data sets with the master or operation data stored in ERP systems.

On top of that, data stakeholders such as analysts or data scientists must deal with multiple requests to different systems and departments to get the authorizations needed to access what they need to do their core roles. These delays may mean the data is late, incomplete, and not properly structured by the time stakeholders get it—even if they can understand it.

Expensive Storage

Data is exponentially generated in an enterprise during regular business activities. Storing and securing data typically incurs more expense (and sometimes greater risk) than value. Cost continues to be a big problem, and not just the cost of housing business data in expensive storage solutions like traditional enterprise data-warehouse platform.

Turning Data into Real Business Value

For more than a decade, Datavard has been helping customers like PostNL get more from their SAP investments by facilitating integration between SAP and Amazon Simple Storage Service (Amazon S3) and Amazon Redshift.

With Datavard Glue, you can easily get data out of any SAP NetWeaver-based system and into a platform like AWS to build next-generation analytics, maximize data availability, and quickly get answers from your data to all of your stakeholders.

Datavard Glue enables you to get a 360-degree view on business events and provides access to insights and intelligence with predictive and even prescriptive analytics not possible in a siloed or fractured approach.

Having all of your data in one place enables superior customer relationship management by analyzing customer preferences and behavior, forecasting sales, predicting trends, and helping you find opportunities for disruption in your market. On top of all that, it allows organizations to increase efficiency around operations and processes by predicting issues and prescribing resolutions before problems even occur.

In short, you can store all of your data in a scalable, performant, and cost-effective manner—including your much needed SAP data in an AWS data lake—to get a unified view of business events and understand what questions you may need answers for in the future.

Solution Architecture

Datavard Glue’s architecture is unique and enables a few distinctive features. It’s built for SAP and needs nothing more than SAP’s system to run. It’s designed as a plugin and the software is delivered using SAP transports.

The solution does not require any additional hardware to be configured and maintained, and all you need to do is import SAP transports in your SAP NetWeaver system and configure the connection to AWS storage services.

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Figure 1 – Datavard Glue reference architecture.

Datavard Glue natively integrates with SAP’s business applications and uses an ABAP-based authorization model. No additional users need to be created, and security is maintained through the familiar SAP authorization framework. This makes it fast to implement and secure.

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Figure 2 – Detailed communication diagram.

Users can access Datavard Glue as they would any other SAP transaction, and can configure the replication of hundreds of tables in just few clicks (levering mass generation functionalities).

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Figure 3 – Datavard Glue cockpit within SAP GUI.

Datavard Glue works with tables and leverages interfaces available in the SAP application layer, like SAP extractors, operational data provisioning, delta info that SAP stores in Advanced Data Store Objects (ADSOs) and Data Store Objects (DSOs), cubes, reports, and function modules.

The solution sports a robust engine that handles steady, stable replication of massive data sets in parallel. It supports push and pull extraction, streaming or batch, and can expertly handle trigger-based deltas or change data capture (CDC).

You can push your data either to Amazon S3 or Amazon Redshift as targets with a choice between batch or streaming replication.

Why Datavard Chose Data Lakes Powered by AWS

The AWS Cloud provides many building blocks required to help implement a secure, flexible, and cost-effective data lake. This centralized repository stores structured and unstructured data at any scale and is powered by an ever-increasing set of AWS analytic services.

With Datavard Glue, you can integrate your SAP data with data generated across your enterprise into your data lake using AWS services to get end-to-end enterprise analytics.

Furthermore, Datavard Glue provides options for customers to tap into SAP data to democratize the data and integrate with diverse data sets into a flexible, cost optimized secure data platform. This enables researchers and scientists to drive process efficiencies and build newer business models using machine learning.

By unifying the data sets from diverse systems and applying intelligence, you can effectively organize the decision making and focus on innovation to help your business operation. This can also help you avoid complex, failing projects that may mean new hardware and proprietary storage and training in new software, support, and maintenance processes.

Because Datavard Glue is deployed as an SAP add-on, installation is not only easy and secure but also reuses SAP GUI as a user interface. This makes it easy for customers familiar with SAP to use. In addition, maintenance and support activities can be integrated into existing SAP processes.

Together with Datavard Glue’s flexible storage management, Amazon S3 and Amazon Redshift’s competitive and scalable pricing means reduced expenditure on several avenues towards overall total cost of ownership (TCO) reduction and the elasticity to adapt whenever you need it to.

Conclusion

Data-driven decisions require access to many disparate types of information. Despite some of the challenges involved with cultivating this approach, viewing data as a strategic asset can give businesses the upper hand in a constantly challenging market.

Consolidating SAP data with the help of Datavard Glue to a central repository such as an AWS data lake enables teams across an enterprise to improve performance measurement, enhance operational efficiency, gain deeper insights and actionable intelligence, and make more informed decisions to support organizational objectives.

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Datavard – AWS Partner Spotlight

Datavard and AWS have been partnering together since 2015. Datavard Glue helps businesses drive value from their SAP data by enabling delta extraction capabilities from and to various data sources.

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