Accelerate your SAS analytics to take the gold

Transcription

1 Accelerate your SAS analytics to take the gold A White Paper by Fuzzy Logix Whatever the nature of your business s analytics environment we are sure you are under increasing pressure to deliver more: more timely insights, more efficiency and more simplicity and all these at a faster return on investment and a lower cost. The growth of data is not helping. According to Gartner & IDC, the volume of data is currently at 2 trillion GB and doubling each year; stated to reach 40 trillion GB by Now, if your analytics is based on SAS, you will probably have to significantly increase your investment each year to perform the same analytics on double the volume of data. It s expensive and time-consuming to manage all that duplicate storage, datacentre-space and network infrastructure for moving data as well as the need to increase processing power. Even with all of that, the analytics may only be performed on subsets of data and the time-to-insights will not live up to your business requirements. So, if you are already seeing red at the additional green you need to shell out each year and are looking to achieve scalability at speed for your analytics at a lower cost, we have a solution. Who are we? Fuzzy Logix pioneered the concept of what the industry now calls in-database analytics. Our flagship product, DB Lytix is a solution that embeds into your existing data warehouse and Hadoop environments, and augments the capability of the underlying warehouse, allowing you to shift heavy processing to the database. By not moving the data, DB Lytix can significantly accelerate the capabilities and see your business achieve phenomenal performance jumps, and potentially huge cost and resource savings. Our 40+ Global Fortune 1000 customers across verticals like retail, financial services, insurance, life sciences, telecom/media, manufacturing and marketing have realized value propositions ranging from 200% to 3,200% in 12 months. Conventional vs In-Database Analytics Traditional approaches to analytics like SAS force you to move data from where it is stored to separate analytics servers because the data is in one place and models run in a different place and then feed the results back into the database. This results in huge penalties: 1

2 High cost of expensive hardware Slow turnaround time as two-thirds of the time is often spent on moving the data Sub-par model analysis due to memory constraints of the analytics servers, models must be built with only what fits into memory, and not the entire data Outdated analysis In many use cases, the underlying database might change rapidly versus the snapshot moved into memory on the analytic servers Even SAS s Hadoop solution requires the purchase of an additional analytics server and requires intra-server data movement which leads to speed and scale penalties. At Fuzzy Logix, we turned the problem on its head with our In-database analytics approach. We move the analytics to data, as opposed to moving data to analytics, and eliminate the need for separate analytics servers. We leverage the full parallelism of today s massively parallel processing databases. With DB Lytix, data scientists are able to build models using very large amounts of data and many variables. No more sampling, no more waiting for data to move from some other place to your analytics server. These models run 10X to 100X faster than traditional analytics bringing you expedited insights at a fraction of the cost. CONVENTIONAL v. IN-DATABASE ANALYTICS Conventional Approach New Approach Visualization Visualization RESULTS REQUEST RESULTS SQL QUERY Logic /Filtering Applied in Analytic Workbench Servers Analytics Logic/Filtering Applied In-Database In-Database Analytics DATA SQL QUERY Database Scalable Performance for Very Large Data 2

3 The Benefits are supported by Real Results Insurance For a large US health insurer, moving the data out of the database to the SAS servers meant breaking it into 25 Jobs and assembling the results a process that took over 6 weeks! Fuzzy Logix in-database analytics allowed the customer to work on the entire dataset at once, and finish the analytics in less than 10 minutes. Financial Services Our AML solution for a large bank found 4 times more cases of suspicious activities than the SAS AML solution and executed in a fraction of the time as SAS. Now the bank scores hundreds of millions of transactions daily in under 15 minutes. Retail For one of the largest retailers in USA, we took a SAS job that ran in 30 hours and reduced that to 30 minutes. We worked on the whole dataset including 80,000 SKUs covering 52 weeks of data for 2,000,000 customers for Market Basket Analytics Our Results Speak for Themselves USE CASE VOLUME SAS TIME DB LYTIX TIME RETAIL: 486 Billi 20 H 2H RETAIL: 80K SKUs 52 wks HEALTHCARE: 25 Jobs MEDIA: BANKING: 25M Households i Augment & Accelerate SAS We know you have already made a substantial investment into your SAS infrastructure and may not want to fully replace it. In that case, we can help you scale at speed, lower your cost and get more out of your existing SAS environment without growing it further. If you would like to fully replace SAS, our consulting team can help you build a plan for a smooth transition. In either case, we can help you: Augment SAS by improving scalability without having to grow the SAS footprint. This is possible because you can actually run our models directly from SAS using PROC SQL. Accelerate your analytics and data science processes by a factor of x to increase the time-to-insight. Not only is the performance faster, but by having all data at your fingertips, companies have told us that their analysts can build ten times as many models with our technology. 3

4 We simply plug in the DB Lytix advanced analytics library into your warehouse or Hadoop environment, and from within SAS or any BI tools that can call SQL, you can push the heavy lifting to the database. Installation can happen in as little as thirty minutes, and does not require a reboot. And all these at less than half the cost compared to constantly adding to the SAS footprint to keep pace with your data. When to call in Fuzzy Logix You can engage with Fuzzy Logix in the following scenarios: 1. A new use case where Fuzzy Logix can be used without SAS A standalone business case to justify the investment in Fuzzy Logix 2. To solve a business pain point where SAS hits a processing wall Slow performance in this case, SAS can pass on some of the heavy lifting into the database to speed up processing Stale analytics on sample data SAS usually requires that data be moved out of the database to the analytics servers before they can be processed. They often cannot handle the entire volume of data to be transported. Thus, resulting in inaccurate and outdated results based on sample data. DB Lytix runs 100% in-database, allowing you to analyse your entire data set on the most current data 3. When it becomes too expensive to scale up SAS Fuzzy Logix allows customers to reduce the size of SAS server farms because of functions deployed in-database and performance characteristics. 4. To replace SAS We get it. You have a lot of code and processes that hold your analytics environment together. It s rare that customers want to do a full replacement of SAS. However, in those cases we can help you create a plan for successful transition. Advantages of Fuzzy Logix over SAS Maintenance and Administration Fuzzy Logix installs easily and quickly usually in 30 minutes You do not need to hire a system administrator Our models inherit your existing security and access rules since we are installed at such a low level in the environment that your existing settings also apply to our models. In-Database Capabilities Fuzzy Logix analytics run in-database; while SAS requires to be moved out of the database to the analytics servers (even in the case of their servers running on your Hadoop nodes). In-database allows for modelling and mining against a full data set with no sampling required Software-only Solution Fuzzy Logix is a software-only solution. No new hardware is needed SAS in-memory analytics needs a separate appliance and/or dedicated memory, and that memory may not be shareable with the database or Hadoop platform Flexibility The Fuzzy Logix interface is SQL and the model results are stored as a structured table in your database allowing easy integration with any front-end visualization tools like Qlik and Tableau. SQL is the number one language used by data scientist. 4

5 The SAS models are proprietary and cannot be embedded into business processes and applications. Performance jumps Our solutions have shown to yield 10X to 100X improvements in performance. Processes that take days will take hours or less; those that take hours will take minutes or less. Improving the Performance of R Fuzzy Logix can make R run faster and leverage the MPP architecture of platforms like enterprise data warehouses & Hadoop through our AdapteR product. Users simply write R, and AdapteR converts it to SQL and executes it in-database. This eliminates issues with memory bounding and performance. SAS In-database vs DB Lytix SAS offers less than 50 models that run in-database. They do offer the ability to build models and deploy them into the database, but the process is complex and you cannot build complex machine learning models DB Lytix is an exhaustive library of 600+ functions, providing the complete breadth of algorithms often used by data scientists to build their models. Broad Platform Consistency SAS was originally built in the late 1970s and has gone through many architectural changes Options include client/server, in-database, in-memory, and cloud All with different architectures Not all analytics run across these options Not all SAS products are integrated SAS has multiple user interfaces/languages including SAS Procedures (PROCs), Enterprise Guide, Visual Analytics, JMP and DS2 The DB Lytix library which has over 600 functions, is available across many enterprise data warehouses e.g. Teradata, IBM PureData, Oracle as well as Hadoop e.g. Hortonworks, Cloudera, MAPR. You can also use it in the cloud e.g. AWS, Azure. If you have computationally intensive problems you can run our models on GPUs via Kinetica or NVIDIA ensuring complete investment protection for the customer. 5

6 Analytics on Hadoop A portion of SAS analytics, not all products, run in-database on Hadoop (some PROCs, Data Quality, Scoring, DS2) SAS on Hadoop requires additional hardware to run the SAS Server and store the data that is extracted from Hadoop in a dedicated bank of memory. Analytics may have to be run on a subset of the data due to memory limitations Very little SAS code can run in-database SASs architecture is inconsistent Fuzzy Logix is a software-only solution running completely in-hadoop. DB Lytix leverages the data access framework and the algorithms are implemented using the user-defined function (UDF) interfaces. This abstraction enables us to inherit all the frameworks supported by the SQL interface. Computational Intensive Problems GPU Based Analytics SAS does not run on GPUs For computationally intensive problems, there is no other technology that can outperform GPUs. Fuzzy Logix has been working with NVIDIA for years and with its latest joint solution created with Kinetica s GPU Accelerated database you can achieve a x performance jump on 1/10th the hardware over CPU-only based solutions Use cases that benefit from GPU based analytics cover a broad range of industries, including: Price SAS is complex to price and expensive to buy For 8 logical processors, SAS costs $185,000; not including client/seat licenses which can cost between $140,000 and $446,000 (depending on product and number of seats) A few more add-ons can easily push the cost to $750,000 or more for 10 users A comparable solution with Fuzzy Logix is almost 70% cheaper while providing full in-database and analytics capabilities that allows pervasive, highly-scalable, real-time analytics that are embeddable in applications. It is clear that a solution designed for the Gigabyte economy will be challenged in the Petabyte economy. However, it is possible to reduce your dependency on SAS (or even eliminate it!). Let us help you move to an analytics environment that was designed to handle tomorrow s data growth, is highly performant, and won t have you spending money year after year in upgrades. For further queries visit or reach out directly to Sonia.virmani@fuzzylogix.com 6