|By Archie Hendryx||
|December 4, 2012 08:00 AM EST||
When the character Maverick from the movie Top Gun exclaimed, "I feel the need, the need for speed", you'd be forgiven for mistaking it for a sound bite from a CIO discussing their transactional databases. Whether it's a financial organization predicting share prices, a bank knowing whether it can approve a loan or a marketing organisation reaching consumers with a compelling promotional offer, the need to access, store, process and analyze data as quickly as possible is an imperative for any business looking to gain a competitive edge. Hence when in 2011, SAP announced their new in-memory platform HANA for enterprise applications everyone took note as they coined the advantage of real-time analytics. SAP HANA promised to not just make databases dramatically faster like traditional business warehouse accelerator systems but instead speed up the front end, enabling companies to run arbitrary, complex queries on billions of records in a matter of seconds as opposed to hours. The vendors of old legacy traditional databases were facing a major challenge, most notably the king of them all...Oracle.
The Birth and Emergence of Big Data
Back in the days of mainframe, you'd find the application and transactional data of reporting databases physically stored in the same system. This was due to applications, operating systems and databases being designed to maximize their hardware resources, which consequently meant you couldn't process transactions and process report simultaneously. The bottleneck here was cost, in that if you wanted to scale you needed another mainframe.
After the advent of client servers where applications could run on a centralized database server via multiple and cost effective servers, scalability was achieved by simply adding additional application servers. Regardless, of this a new bottleneck was quickly established with systems relying on a single database server and requests from ever increasing application servers that ended up causing I/O stagnation. This problem became exasperated with OLTP (online transaction processing), where report creation required the system to concurrently read multiple tables in the database. Added to this servers and processors kept getting faster while disks (despite the emergence of SSD) were quickly becoming the bottleneck to automated processes that were producing large amounts of data that concurrently resulted in more report requests.
The net effect was a downward spiral where the increase of users requiring an increase of reports from the databases meant an increase in huge amounts of data being requested from disks that simply weren't up to the job. When you then factored in the data proliferation of external users caused by the Internet and pressure inducing laws such as Sarbanes-Oxley, the demand to analyze even more data even quicker has reached fever point. With data and user volumes increasing by a factor of thousands compared to the I/O capability of databases, the transaction-based industry faced a challenge that required a dramatic shift and change. Cue the 2011 emergence of SAP's HANA.
Real-Time In Memory Platform Presents a Groundbreaking Approach
One of the major advantages of SAP HANA's ability to run in real time is that it offers a non-requirement for data redundancy as it's built to run as a single database. With clusters of affordable and scalable servers, transactional and analytical data are run on the same database, hence eliminating different types of databases for different application needs. Oracle on the other hand has built an empire on exactly the opposite.
Oracle has thrived on a model where generally companies start with a simple database that's utilized for checking sales orders and ensuring product delivery to customers but as the business grows they need more databases with different and more demanding functions. Functions such as managing customer relationships, complex reporting and analysis drives a need for new databases that are separate from the actual business requiring data to be moved from one system to another. Eventually you have a sprawl of databases as existing ones are unable to handle the workloads making it almost impossible to track data movements yet alone attain real time updates. So while the Oracle marketing machine is also pitching the benefits of in-memory via its Exalytics appliance and in-memory database, TimesTen, Oracle are certainly in no rush to break this traditional model of database sprawl and the money-spinning licenses that come with it.
Looking closely at the Oracle Exalytics / TimesTen package, despite the hype, it merely is just an add-on product meaning that an end user will still need a license for the transactional database, another license for the data warehouse database and yet another license for TimesTen for Oracle Exalytics.
Moreover, the Oracle bolt-on approach serves to sell more of their hardware commodity and in some ways perversely justify their acquisition of SUN Microsystems, all at the expense of the customer. Due to the Exalytics approach continuing the traditional requirement for transactional data to be duplicated from the application to the warehouse and once again to Exalytics, the end user not only ends up with three copies of the data, they also have to have three levels of storage and servers. In contrast SAP HANA is designed to be a single database that runs both transactional applications and Business Warehouse deployments. Not only does SAP HANA's one copy of data replace the two or three required for Oracle it also eliminates the need for materialized views, redundant aggregates and indexes leaving a significantly reduced data footprint.
Comparing HANA to Oracle's TimesTen and Exalytics
As expected Oracle have already initiated their FUD team with bogus claims and untruths against HANA as well as even pushing their TimesTen as a like for like comparison. Where this is hugely flawed is that they fail to acknowledge or admit that SAP HANA is a completely groundbreaking design as opposed to a bolt-on approach. With SAP HANA data is completely managed and accessed in RAM consequently doing away with the requirement of MOLAP, multiple indexes and other tuning features that Oracle pride themselves on.
Furthermore, despite the Oracle FUD, SAP HANA does indeed handle both unstructured and structured data, as well as utilise parallel queries for scaling out across server nodes. In this instance Oracle are trying hard to create the most confusion and subsequently detract the market from realizing that the TimesTen with Exalytics package still can't scale out beyond the 1TB RAM limit unlike SAP HANA where each container can store up to 500TB of data all executable at high speed.
With an aggressive TCO and ROI model compared to a traditional Oracle deployment, SAP HANA also proves a lot more cost effective. With pricing based on an incremental of 64GB RAM and the total amount of data held in memory, licenses are fully inclusive of production and test/development requirements as well as the necessary tools.
SAP HANA's Embracing of VMware
Furthermore with Oracle's belligerent stance towards VMware and the cost savings it brings to end users, SAP on the other hand has embraced it. The recent announcement that SAP HANA is supporting VMware vSphere will provide them a vast competitive advantdge, as it will enable customers to provision instances of SAP HANA in minutes as VM templates, as well as gain benefits such as Dynamic Resource Scheduling and vSphere vMotion. By virtualizing SAP HANA with VMware, end users can quickly have several smaller HANA instances all sharing a single physical server leading to better utilization of existing resources. With the promise of certified preconfigured and optimised converged infrastructures such as the Vblock around the corner, SAP HANA appliances could be shipped with vSphere 5 and SAP HANA pre-installed within days, enabling rapid deployment for businesses.
The Business Benefits of Real-Time
With business and transactions being done in real time, SAP HANA ensures that the data and the analytics that come with them are also in real time. The process of manually polling data from multiple systems and sorting them through are inadequate in a time when businesses are facing unpredictable economic conditions and volatile demand and complex supply chains. The need is for real time metrics that are aligned to supply and demand where a retailers' shelves can accurately and immediately be stocked eliminating unnecessary inventory costs, lost sales opportunities and failed product launches. Being able to instantly analyze data at any level of granularity enables a business to quickly respond to these market insights and take decisive actions such as transferring inventory between distribution centers based on expected sales or altering the prices of promotions based on customer demand. Instead of waiting for processes that take hours, days or even weeks, SAP HANA's real time capabilities enable businesses to react in real time to incidents.
Ultimately SAP HANA is a revolutionary step forward that will empower organizations to focus more on the business and less on the infrastructure that supports them. With the promise of new applications being built by SAP to support real time decision making as well being able to run existing applications, SAP HANA presents the opportunity to not only transform a business but also the underlying technology that supports it.
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