Sr Director SAP Big Data Product Marketing, Technology & Innovation Platform

Big Data offers analysts and data scientists the opportunity to build more sophisticated and more accurate predictive models than before, but without the right data environment, it’s not easy. It requires an in-memory architecture that supports thousands of columns and billions of rows and a predictive analytics tool that can harness that architecture, such as SAP BusinessObjects Predictive Analytics.

Twentieth-century technology is insufficient. Blame it on the disk. Back in the 1980s, database engineers saw a world where memory was extremely expensive. Just one terabyte of RAM cost over $100 million US dollars. Today, we can get it for less than $5,000 US dollars. So, vendors built database architectures centered on the disk.

In a Big Data world, the disk is simply too slow. Consider this: reading 1 petabyte of data off a disk sequentially – i.e. no seeking, just end-to-end straight off the disk – takes 58 days using the fastest hard disk available today (according to the Tom’s Hardware website). SSD definitely speeds things up: two days with the fastest SSD RAID. It’ll cost millions to buy, though.

In many ways, Big Data is a real-time data access problem. That’s precisely why innovators are developing new ways to store and process data, all in an effort to get around the hard disk bottleneck. All of the approaches, in essence, minimize the bottleneck in order to improve response time.

Distributed Computing

Distributed computing spreads a lot of data across many disks that can all be read simultaneously. Hadoop builds on the concept of distributed computing, but opens up the platform to handle any data set with any arbitrarily designed algorithm. To overcome the disk, the Hadoop community built Apache Spark, which provides a distributed data processing architecture, like Hadoop HDFS, that operates in-memory across commodity hardware.

Columnar Databases

Like distributed databases and Hadoop, columnar databases optimize data storage architecture in order to reduce the amount of data read off any one disk. It does this by grouping related attributes, or columns, together. The assumption is that most analytical queries only use a subset of columns, so you should only access data related to those specific columns. They also highly compress the data, further reducing the number of bits read off disk.

In-Memory Databases

In-memory databases take it to a whole new level by removing the disk from the equation altogether. It leverages the power of today’s processors to read and analyze data at a raw speed that’s 1,000 to 10,000 times faster than reading data off the disk. In some cases, customers have experienced performance gains of 100,000 times faster. How?

–   Compress the data with in-memory columnar data stores

–   Move the data accessed most often into L1 caches on the chip

That’s why we are so bullish about in-memory and the SAP HANA platform for Big Data. That’s not to say disk solutions don’t have a role to play. But…at the core you want an in-memory system that can run algorithms where your data is. No moving the data to the algorithms, that doesn’t work in a Big Data world. Instead, move the core algorithms into the data system.

SAP BusinessObjects Predictive Analytics

SAP BusinessObjects Predictive Analytics is the right tool for business analysts and data scientists to build predictive models from Big Data. First and foremost, it can analyze data inside SAP HANA and Apache Spark. There’s no need to transfer data out of these environments for processing. Rather, the SAP BusinessObjects Predictive Analytics processing engine can run inside these tools –  dramatically improving performance.

SAP BusinessObjects Predictive Analytics is also able to analyze exceptionally wide datasets. In fact, you can have up 15,000 columns in a dataset, while other tools support only a few hundred to 1,000 columns at most. This ensures that your predictive models provide the greatest level of accuracy possible.

Big Data is radically altering our world. It’s a game changer. For those who grab hold of it, you have an opportunity to propel your business forward – and the surest way forward is with SAP BusinessObjects Predictive Analytics running on SAP HANA or Apache Spark. It is the best combination for building predictive models on Big Data, whether you’re a business analyst or data scientist.

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Every week at Amick Brown, we are questioned about HANA There is a lot of confusion in the market with the multiple options. As well, there are many questions about timing and business application. You have asked, so we will start a series of HANA articles to address your questions.

SAP Business Suite 4 SAP HANA, shortened to SAP S/4HANA,  is a big strategic play from SAP. Here is why you need to take heed.

The new SAP S/4HANA is supposed to replace the SAP Business Suite (formerly R/3) over the next few years. This announcement and the launch of the software lay a roadmap for SAP in the coming years.

What led to this launch?

SAP is a leader in ERP worldwide. However, in the recent past, a new trend is taking over in the business world. Cloud-based software services also known as Software-as-a-Service. SAP has SaaS components to it, but its main business model has been selling software the old way: software installed at the customers’ premise.

Other cloud companies  have been slowly chipping away at SAP’s market share. And this is SAP’s answer.

Name

The R in R/3 stood for real-time. The S in the S/4 stands for Simple. This is the big idea. SAP is planning on simplifying the ERP system with this release.

Database

While SAP R/3 Business Suite ran on any database, S/4 runs exclusively on HANA. SAP has spent considerable financial resources and effort on building up the in-memory database over the past few years. SAP HANA has tremendous performance advantages compared to the older disk-based database solutions. This large-scale change has enabled SAP to dramatically simplify both the data-model as well as the user-experience.

One significant aspect of HANA is that it is an in-memory appliance. This means data-access times (disk read/writes) are not an issue anymore, allowing developers to focus more on business logic than performance. This lends itself to the other motivation for S/4 – simplicity.

The Cloud

SAP S/4HANA is mostly a movement of SAP’s premier software from customer premise to the cloud. However, on-premise solutions will still be available. SAP offers 3 options:

1. Public Cloud – Completely managed by SAP. Multi-tenancy shared by all public cloud customers
2. Private Cloud – Partially managed by SAP. Exclusive database per customer.
3. On-Premise – Software installed on client’s hardware. Client pays for user-licenses.

Software

SAP S/4HANA will allow customization to S/4HANA on the HANA Cloud Platform (HCP). This means ABAP developers will get to continue to use their skills. If you don’t know OO, it is a good time to learn it.

UI

Let’s admit it, SAP R/3 has not been known for its stellar user-experience. UI on SAP R/3 has been clunky, rigid and unwelcoming. But the S/4HANA user-interface will be based on SAP’s Fiori UX platform. SAP Fiori, launched earlier in 2014, gives the software a new look-and-feel. The fact it does not have licensing cost should make it attractive to customers with an existing SAP installation.

Conclusion

Co-founder, Hasso Plattner said “If this doesn’t work, we’re dead. Flat-out dead.” This may be just Hasso Plattner being the passionate visionary that he is. But this indeed is a huge launch from SAP, one whose initial roll out is expected to be 3-5 years, and customer transitions lasting more 10 years.

The story on S/4 HANA continues here.  Watch this space and Follow Amick Brown on LinkedIn

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