Monthly Archives: July 2014

Garbage In-Memory, Expensive Garbage

July 7, 2014

A first anniversary is always special and in May I marked my first with Teradata. In my previous lives I celebrated almost ten years with Shell and seventeen years creating my own businesses focused on data warehousing and business intelligence solutions for SAP. With my last business “NewFrontiers” I leveraged all twenty seven years of ERP experiences to develop a shrink wrapped solution to enable SAP analytics. 

Through my first anniversary with Teradata, all this time, the logical design of SAP has been the same. To be clear, when I say SAP, I mean R/3 or ‘R/2 with a mouse’ if you’re old enough to remember. Today R/3 is also known as the SAP Business suite, ERP or whatever. Anyway, when I talk about SAP I mean the application that made the company rightfully world famous and that is used for transaction processing by almost all large multinational businesses.

My core responsibility at Teradata is the engineering of the analytical solution for SAP. My first order of business was focusing my team on delivering an end-to-end business analytic product suite to analyze ERP data that is optimized for Teradata. Since completing our first release, my attention turned to adding new features to help companies take their SAP analytics to the next level. To this end, my team is just putting the finishing touches on a near real-time capability based on data replication technology. This will definitely be the topic of upcoming blogs.

Over the past year, the integration and optimization process has greatly expanded my understanding of the differentiated Teradata capabilities. The one capability that draws in the attention of types like me ‘SAP guys and girls’ is Teradata Intelligent Memory. In-memory computing has become a popular topic in the SAP community and the computer’s main memory is an important part of Teradata’s Intelligent Memory. However Intelligent Memory is more than “In-Memory” — because with Intelligent Memory, the database addresses the fact that not all memory is created equal and delivers a solution that uses the “right memory for the right purpose”. In this solution, the most frequently used data – the hottest — is stored In-Memory; the warm data is processed from a solid state drive (SSD), and colder, less frequently accessed data from a hard disc drive (HDD). This solution allows your business to make decisions on all of your SAP and non-SAP data while coupling in-memory performance with spinning disc economics.

This concept of using the “right memory for the right purpose” is very compelling for our Teradata Analytics for SAP solutions. Often when I explain what Teradata Analytics for SAP Solutions does, I draw a line between DATA and CONTEXT. Computers need DATA like cars need fuel and the CONTEXT is where you drive the car. Most people do not go the same place every time but they do go to some places more frequently than others (e.g. work, freeways, coffee shops) and under more time pressure (e.g. traffic).

In this analogy, most organizations almost always start building an “SAP data warehouse” by loading all DATA kept in the production database of the ERP system. We call that process the initial load. In the Teradata world we often have to do this multiple times because when building an integrated data warehouse it usually involves sourcing from multiple SAP ERPs. Typically, these ERPs vary in age, history, version, governance, MDM, etc. Archival is a non-trivial process in the SAP world and the majority of the SAP systems I have seen are carrying many years of old data . Loading all this SAP data In-Memory is an expensive and reckless thing to do.

Teradata Intelligent Memory provides CONTEXT by storing the hot SAP data In-Memory, guaranteeing lightning fast response times. It then automatically moves the less frequently accessed data to lower cost and performance discs across the SSD and HDD media spectrum. The resulting combination of Teradata Analytics for SAP coupled with Teradata’s Intelligent Memory delivers in-memory performance with very high memory hit rates at a fraction of the cost of ‘In-Memory’ solutions. And in this business, costs are a huge priority.

The title of this Blog is a variation on the good old “Garbage In Garbage Out / GIGO” phrase; In-Memory is a great feature, but not all data needs to go there! Make use of it in an intelligent way and don’t use it as a garbage dump because for that it is too expensive.

Patrick Teunissen is the Engineering Director at Teradata responsible for the Research & Development of the Teradata Analytics for SAP® Solutions at Teradata Labs in the Netherlands. He is the founder of NewFrontiers which was acquired by Teradata in May 2013.

1 Needless to say I am referring to SAP’s HANA database developments.

2 Data that is older than 2 years can be classified as old. Transactions, like sales and costs are often compared with the a budget/plan and the previous year. Sometimes with the year before that but hardly ever with data older than that.

ThINking INSIDE the Box: Dramatically Increase Analytic Performance

July 1, 2014

Thinking inside the box when it comes to your data has many advantages — compared to processing outside the box. Organizations are collecting more structured and unstructured data than ever before, and it is presenting great opportunities and challenges to analyze ALL of that complex data. In this volatile and competitive economy, there has never been a bigger need for proactive and agile strategies to overcome these challenges by applying the analytics directly to the data rather than shuffling data around. The point: There are two key technologies that dramatically improve and increase performance when analyzing big data: “in”-database and “in”-memory analytics.

Think inside the Teradata Box!

Think inside the Teradata Box!

“In-database” analytics refers to the integration of advanced analytics into the data warehousing platform functionality. Many analytical computing solutions and large databases use this approach because it provides significant performance improvements over the traditional methods. Thus, in-database analytics have been adopted by many SAS business analysts who have been able to realize the valuable business benefits of streamlined processing and increased performance.

Pardon my commercial tone, but honestly, with SAS® in-database analytics for Teradata, SAS users have the ability to develop complex data models and score the model in the data warehouse. By doing so, it removes the need to either move or extract the data to a SAS environment or convert the analytical code to something that could be executed on the data platform.

Now for the first time, users can leverage “in-memory” analytics to analyze large volumes of data in-memory with SAS® High-Performance Analytics (HPA) products and SAS® Visual Analytics for Teradata. This latest innovation provides an entirely new approach to tackle big data by using an in-memory analytics engine to deliver super-fast responses to complex analytical problems. It is a set of products beyond SAS Foundation technologies to explore and develop data models using all of your data. Jointly developed with SAS, the Teradata Appliance for SAS High-Performance Analytics, Model 720 eliminates the need to copy data to a separate appliance with dedicated SAS nodes for in-memory processing.

Teradata and SAS have joined forces to revolutionize your business by providing enterprise analytics in a harmonious data management platform to deliver critical strategic insights by applying advanced analytics “inside” the database or data warehouse where the vast volume of data is fed and resides.

This “inside job” approach to managing data is producing huge, positive business results: We have customers using both technologies which are complementary and have experienced dramatic increases in performance and value. Analyses that took days or weeks are now down to hours and minutes by integrating in-database and in-memory analytics inside Teradata. That is why I strongly believe “thinking inside the box” is a key strategic innovation when it comes to big data analytics. A recent webcast was conducted by IIA, Teradata and SAS on this topic. To view the webcast, click here.

Guest blogger – Tho Nguyen