This article is a continuation of my first article, 25 Big Data terms everyone should know. Since it got such an overwhelmingly positive response, I decided to add an extra 50 terms to the list.  Just to give you a quick recap, I covered the following terms in my first article: Algorithm, Analytics, Descriptive analytics, Prescriptive analytics, Predictive analytics, Batch processing, Cassandra, Cloud computing, Cluster computing, Dark Data, Data Lake, Data mining, Data Scientist, Distributed file system, ETL, Hadoop, In-memory computing, IOT, Machine learning, Mapreduce, NoSQL, R, Spark, Stream processing, Structured Vs. Unstructured Data.

Now let’s get on with 50 more big data terms.

Apache Software Foundation (ASF) provides many of Big Data open source projects and currently there are more than 350 projects. I could be spending my whole life just explaining these projects so instead I picked few popular terms.

Apache Kafka: Kafka, named after that famous czech writer, is used for building real-time data pipelines and streaming apps. Why is it so popular? Because it enables storing, managing, and processing of streams of data in a fault-tolerant way and supposedly ‘wicked fast’. Given that social network environment deals with streams of data, Kafka is currently very popular.

Apache Mahout: Mahout provides a library of pre-made algorithms for machine learning and data mining and also an environment to create more algorithms. In other words, an environment in heaven for machine learning geeks. Machine learning and Data mining are covered in my previous article mentioned above.

Apache Oozie: In any programming environment, you need some workflow system to schedule and run jobs in a predefined manner and with defined dependencies. Oozie provides that for Big Data jobs written in languages like pig, MapReduce, and Hive.

Apache Drill, Apache Impala, Apache Spark SQL

All these provide quick and interactive SQL like interactions with Apache Hadoop data. These are useful if you already know SQL and work with data stored in big data format (i.e. HBase or HDFS). Sorry for being little geeky here.

Apache Hive: Know SQL? Then you are in good hands with Hive. Huve facilitates reading, writing, and managing large datasets residing in distributed storage using SQL.

Apache Pig: Pig is a platform for creating query execution routines on large, distributed data sets. The scripting language used is called Pig Latin (No, I didn’t make it up, believe me). Pig is supposedly easy to understand and learn. But my question is how many of these can one learn?

Apache Sqoop: A tool for moving data from Hadoop to non-Hadoop data stores like data warehouses and relational databases.

Apache Storm: A free and open source real-time distributed computing system. It makes it easier to process unstructured data continuously with instantaneous processing, which uses Hadoop for batch processing.

Artificial Intelligence (AI) – Why is AI here? Isn’t it a separate field you might ask. All these trending technologies are so connected that it’s better for us to just keep quiet and keep learning, OK? AI is about developing intelligence machines and software in such a way that this combination of hardware and software is capable of perceiving the environment and take necessary action when required and keep learning from those actions. Sounds similar to machine learning? Join my ‘confused’ club.

Behavioral Analytics: Ever wondered how google serves the ads about products / services that you seem to need? Behavioral Analytics focuses on understanding what consumers and applications do, as well as how and why they act in certain ways. It is about making sense of our web surfing patterns, social media interactions, our ecommerce actions (shopping carts etc.) and connect these unrelated data points and attempt to predict outcomes. Case in point, I received a call from a resort vacations line right after I abandoned a shopping cart while looking for a hotel. Need I say more?

Brontobytes–  1 followed by 27 zeroes and this is the  size of the digital universe tomorrow. While we are here, let me talk about Terabyte, Petabyte, Exabyte, Zetabyte, Yottabyte, and Brontobyte. You must read this article to know more about all these terms.

Business Intelligence (BI): I’ll reuse Gartner’s definition of BI as it does a pretty good job. Business intelligence (BI) is an umbrella term that includes the applications, infrastructure and tools, and best practices that enable access to and analysis of information to improve and optimize decisions and performance.

Biometrics: This is all the James Bondish technology combined with analytics to identify people by one or more of their physical traits, such as face recognition, iris recognition, fingerprint recognition, etc.

Clickstream analytics: This deals with analyzing users’ online clicks as they are surfing through the web. Ever wondered why certain Google Ads keep following you even when switched websites etc? Big brother knows what you are clicking.

Cluster Analysis is an explorative analysis that tries to identify structures within the data.  Cluster analysis is also called segmentation analysis or taxonomy analysis.  More specifically, it tries to identify homogenous groups of cases, i.e., observations, participants, respondents.  Cluster analysis is used to identify groups of cases if the grouping is not previously known.  Because it is explorative it does make any distinction between dependent and independent variables.  The different cluster analysis methods that SPSS offers can handle binary, nominal, ordinal, and scale (interval or ratio) data.

Comparative Analytics: I’ll be going little deeper into analysis in this article as big data’s holy grail is in analytics. Comparative analysis, as the name suggests, is about comparing multiple processes, data sets or other objects using statistical techniques such as pattern analysis, filtering and decision-tree analytics etc. I know it’s getting little technical but I can’t completely avoid the jargon. Comparative analysis can be used in healthcare to compare large volumes of medical records, documents, images etc. for more effective and hopefully accurate medical diagnoses.

Connection Analytics: You must have seen these spider web like charts connecting people with topics etc to identify influencers in certain topics. Connection analytics is the one that helps to discover these interrelated connections and influences between people, products, and systems within a network or even combining data from multiple networks.

Data Analyst: Data Analyst is an extremely important and popular job as it deals with collecting, manipulating and analyzing data in addition to preparing reports. I’ll be coming up with a more exhaustive article on data analysts.

Data Cleansing: This is somewhat self-explanatory and it deals with detecting and correcting or removing inaccurate data or records from a database. Remember ‘dirty data’? Well, using a combination of manual and automated tools and algorithms, data analysts can correct and enrich data to improve its quality. Remember, dirty data leads to wrong analysis and bad decisions.

DaaS: You have SaaS, PaaS and now DaaS which stands for Data-as-a-Service. DaaS providers can help get high quality data quickly by by giving on-demand access to cloud hosted data to customers.

Data virtualization – It is an approach to data management that allows an application to retrieve and manipulate data without requiring technical details of where it stored and how it is formatted etc. For example, this is the approach used by social networks to store our photos on their networks.

Dirty Data: Now that Big Data has become sexy, people just start adding adjectives to Data to come up with new terms like dark data, dirty data, small data, and now smart data. Come on guys, give me a break, Dirty data is data that is not clean or in other words inaccurate, duplicated and inconsistent data. Obviously, you don’t want to be associated with dirty data.Fix it fast.

Fuzzy logic: How often are we certain about anything like 100% right? Very rare. Our brains aggregate data into partial truths which are again abstracted into some kind of thresholds that will dictate our reactions. Fuzzy logic is a kind of computing meant to mimic human brains by working off of partial truths as opposed to absolute truths like ‘0’ and ‘1’ like rest of boolean algebra. Heavily used in natural language processing, fuzzy logic has made its way into other data related disciplines as well.

Gamification: In a typical game, you have elements like scoring points, competing with others, and certain play rules etc. Gamification in big data is using those concepts to collecting data or analyzing data or generally motivating users.

Graph Databases: Graph databases use concepts such as nodes and edges representing people/businesses and their interrelationships to mine data from social media. Ever wondered how Amazon tells you what other products people bought when you are trying to buy a product? Yup, Graph database!

Hadoop User Experience (Hue): Hue is an open-source interface which makes it easier to use Apache Hadoop. It is a web-based application and has a file browser for HDFS, a job designer for MapReduce, an Oozie Application for making coordinators and workflows, a Shell, an Impala and Hive UI, and a group of Hadoop APIs.

HANA: High-performance Analytical Application – a software/hardware in-memory platform from SAP, designed for high volume data transactions and analytics.

HBase: A distributed, column-oriented database. It uses HDFS for its underlying storage, and supports both batch-style computations using MapReduce and transactional interactive

Load balancing: Distributing workload across multiple computers or servers in order to achieve optimal results and utilization of the system

Metadata: “Metadata is data that describes other data. Metadata summarizes basic information about data, which can make finding and working with particular instances of data easier. For example, author, date created and date modified and file size are very basic document metadata. In addition to document files, metadata is used for images, videos, spreadsheets and web pages.” Source: TechTarget

MongoDB: MongoDB is a cross-platform, open-source database that uses a document-oriented data model, rather than a traditional table-based relational database structure. This type of database structure is designed to make the integration of structured and unstructured data in certain types of applications easier and faster.

Mashup: Fortunately, this term has similar definition of how we understand mashup in our daily lives. Essentially, mashup is a method of merging different datasets into a single application (Examples: Combining real estate listings with demographic data or geographic data). It’s really cool for visualization.

Multi-Dimensional Databases: A database optimized for data online analytical processing (OLAP) applications and for data warehousing.Just in case you are wondering about data warehouses, it is nothing but a central repository of data multiple data sources.

MultiValue Databases: They are a type of NoSQL and multidimensional databases that understand 3 dimensional data directly. They are good for manipulating HTML and XML strings directly for example.

Natural Language Processing: Software algorithms designed to allow computers to more accurately understand everyday human speech, allowing us to interact more naturally and efficiently with them.

Neural Network: As per http://neuralnetworksanddeeplearning.com/, Neural networks is a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data. It’s been a long time since someone called a programming paradigm ‘beautiful. In essence, artificial neural networks are models inspired by the real-life biology of the brain.. Closely related to this neural networks is the term Deep Learning.  Deep learning, a powerful set of techniques for learning in neural networks.

Pattern Recognition: Pattern recognition occurs when an algorithm locates recurrences or regularities within large data sets or across disparate data sets. It is closely linked and even considered synonymous with machine learning and data mining. This visibility can help researchers discover insights or reach conclusions that would otherwise be obscured.

RFID: Radio Frequency Identification; a type of sensor using wireless non-contact radio-frequency electromagnetic fields to transfer data. With Internet Of Things revolution, RFID tags can be embedded into every possible ‘thing’ to generate monumental amount of data that needs to be analyzed. Welcome to the data world 🙂

SaaS: Software-as-a-Service enables vendors to host an application and make it available via the internet. SaaS providers provide services over the cloud.

Semi-structured data: Semi-structured data refers to data that is not captured or formatted in conventional ways, such as those associated with a traditional database fields or common data models. It is also not raw or totally unstructured and may contain some data tables, tags or other structural elements. Graphs and tables, XML documents and email are examples of semi-structured data, which is very prevalent across the World Wide Web and is often found in object-oriented databases.

Sentiment Analysis: Sentiment analysis involves the capture and tracking of opinions, emotions or feelings expressed by consumers in various types of interactions or documents, including social media, calls to customer service representatives, surveys and the like. Text analytics and natural language processing are typical activities within a process of sentiment analysis. The goal is to determine or assess the sentiments or attitudes expressed toward a company, product, service, person or event.

Spatial analysis refers to analysing spatial data such geographic data or topological data to identify and understand patterns and regularities within data distributed in geographic space.

Stream processing is designed to act on real-time and streaming data with “continuous” queries. With data that is constantly streaming from social networks, there is a definite need for stream processing and also streaming analytics to continuously calculate mathematical or statistical analytics on the fly within these streams to handle high volume in real time.

Smart Data is supposedly the data that is useful and actionable after some filtering done by algorithms.

Terabyte: A relatively large unit of digital data, one Terabyte (TB) equals 1,000 Gigabytes. It has been estimated that 10 Terabytes could hold the entire printed collection of the U.S. Library of Congress, while a single TB could hold 1,000 copies of the Encyclopedia Brittanica.  You must read this article to know more about all these terms.

Visualization – with the right visualizations, raw data can be put to use. Visualizations of course do not mean ordinary graphs or pie-charts. They mean complex graphs that can include many variables of data while still remaining understandable and readable

Yottabytes– approximately 1000 Zettabytes, or 250 trillion DVD’s. The entire digital universe today is 1 Yottabyte and this will double every 18 months. You must read this article to know more about all these terms.

Zettabytes – approximately 1000 Exabytes or 1 billion terabytes.  You must read this article to know more about all these terms.

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The German software giant SAP announced yesterday that it has expanded its integration with Esri, a mapping specialist that provides geospatial services and content. The partnership brings high-performance spatial analytics, self-service mapping and collaboration to geographic information system (GIS) and business users to allow them to leverage real-time location intelligence in both their Esri and SAP environments.

The announcement will see Esri’s mapping technology integrated across the SAP Hana in-memory database, core SAP enterprise applications, the BusinessObjects analytics portfolio, and the SAP Mobile platform. Taking advantage of Hana’s rapid processing times, customers can now avoid moving data out of Hana and into Esri’s ArcGIS server – instead ArcGIS querying can take place natively inside SAP Hana, where the data is stored.

Moreover, as one article reports, the extended integration will also support ArcGIS self-service mapping and collaboration features with SAP Hana as a high-performance engine of the business data that is geospatially enabled.

Steve Lucas, president of platform solutions for SAP, said in a statement,

“By integrating ESRI’s industry leading GIS with SAP HANA, the SAP BusinessObjects BI platform and SAP Mobile Platform as well as geospatial analytics within SAP Lumira, we are enriching business data with geographical context and presenting it in real time — bringing a whole new level of insight to customers.”

Read more here

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SAP announced yesterday two initiatives that will aim to use Big Data to help better understand the planet’s diversity and food safety. The announcement, which was made during the SAP Big Data for Industry Influencer Event, said that the initiatives would involve the use of SAP HANA to collect and analyse data to help crowdsource the identification and analysis of species globally as well as identify fraud in the global food supply.

Understanding the Planet’s Diversity 

In an effort to understand the planet’s diversity better, SAP is working with The International Barcode of Life (iBOL) to collect and analyze data that can contribute to iBOL’s existing database of more than 400,000 species. The iBOL project is an ambitious programme that aims to identify all the species on the planet – estimated to be anywhere between 10 million to 100 million species – and has called upon SAP to expand its research. The urgency of the task, the company said, is clear “as species are disappearing from the planet at an alarming rate.”

To tackle the problem, SAP have built an applications with iBOL called LifeScanner to crowdsource the collection and analysis of all this information. The application allows anyone using an iPhone to collect a tissue sample or whole organism, send it off for analysis and get species identification using DNA barcodes from anywhere on the globe. The published DNA barcode is then made available to researchers and students for analysis using SAP HANA, SAP Lumira software as well as other third-party analytics tools.

“Whether it’s better understanding the human species, or any other animal or planet species, there is no doubt that Big Data gives us the opportunity to better understand life all around us,” said Irfan Khan, senior vice president and general manager, Database & Technology, SAP. “The volumes and sources of data continue to grow rapidly and using this information intelligently can help prevent the extinction of species, promote new life science discoveries and improve the health of life on our planet. The SAP HANA platform helps organizations better process Big Data so they can acquire, analyze and act on insights in real time.”

Combatting Identity Fraud in the Food Supply Chain

As SAP explained, traceability of food sources and confirmation of food product authenticity is a difficult and time-consuming challenge.

“Most global supply chain visibility solutions in the food industry ensure strict processes and track packaging, but knowing what species are inside the package is challenging because many species can be hard to identify after processing,” the company said in a press release.

To combat the problem of identify fraud in the food supply chain, SAP is working with Tru-ID to integrate DNA-based verification testing into the supply chain and help companies identify the “source of adulteration among their suppliers.” By usining SAP HANA Cloud Platform, organisations can ask suppliers to share independently audited tests.

The company said in a statement,

“Customers are envisioned to be able to integrate these test results into supply chain visibility solutions from SAP, so they can address problems that may arise from food-quality problems such as determining the quality of the foods supplied by certain suppliers, initiating product recalls when food contamination or substitution is detected and identifying risk exposure by supplier and product.”

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In an effort to improve its response to buying trends and costumer preferences, sportswear giant Adidas
is now starting to use the MicrosStrategy’s analytics platform which is based on SAP’s cloud-based in-
memory computing platform HANA.

Michael Voegele, Vice President of Global IT and Head of Enterprise Architecture at Adidas Group, phrases
the company’s aim like this: “We want to get more social competitor insights from the web, combined
with our information gathered through our backend systems and other environments.” According to
Voegele this will be achieved through big data analysis: ”In-memory computing, Internet of Things, all
of this we use for strategic big data and actionable insights. That’s what we see as part of a solution to get us closer to our consumers.”

Voegele raises the question “How do you convert from the old style financial reporting towards something that helps you predict what is going to predict what is going to happen in the marketplace,
going to predict what consumers will like, going to influence the consumer with regards to their
purchasing decisions?”

The MicroStrategy analytics platform will serve as the front-end of a combination of four different
data warehouses. Its’s data is collected from the company’s traditional databases, costumer information
that is gathered from their CRM platform and core data sourced from Hadoop clusters.

Voegele explained that Adidas wanted to embrace a faster approach to actually delivering insights to its
business partners, so it sought to provide its global outfit with more BI self-service capabilities,
enabling them to create their own dashboards. The company also prioritised making its analytics
available on mobile platforms. This way the company hopes to communicate and work more
effectively with it’s business partners and customers, as well as to become more flexible in response to
its competitors.

The geographical distances between Adidas and its wholesale, retail and online customers have grown,
some spanning as far as 80.000 km. But the brand is not planning to lose the direct connection to its
individual customers. Describing the nature of the project, Voegele says “Clearly we do see BI as the
foundation to understand each and every consumer – but we’re not talking aggregates. We don’t want
to look at aggregates, we want to look at the single and consumer and make sure he gets a great
experience with Adidas group.” Clearly stating the aim of this project, he concludes: “And hopefully,
at the end of the day, that helps bring us closer to our consumers.”

Read more here.

(image credit: Oscar Chavez)

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We spoke with the VP of Product Strategy at Birst, Southard Jones, ahead of the announcement of Birst’s support for SAP HANA. Birst provides an enterprise-calibre Business Intelligence platform based on the cloud. Its approach is designed to be less costly and more agile than Legacy BI and more powerful than Data Discovery. Birst gives business teams the ability to solve problems using data in new ways, while maintaining a uniform approach to how that business information is managed.

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Can you give us a brief overview of this announcement?

The idea of in-memory computing, from an analytic perspective, has been talked about for some time now, primarily because of the lightning speed response rates you can get. Depending on what benchmark you read, HANA can query billions of records in close to a second. So far we’ve run a number of those on Birst as part of this support effort, and what we’ve found is that this is accurate for us too.

We first ran a keynote benchmark on one of our larger customer databases and it took 45 seconds to return that query. Then we tried it on a column store database called Infobright and that took about two and half seconds. But when we tried this with HANA it took a staggering 100 milliseconds. 100 milliseconds for a database that has over a billion records is nothing short of astronomical. So, speed is absolutely there with HANA and that’s why we chose it as our preferred in-memory database.

What is it about HANA that makes it so appealing to Birst?

Originally, Birst ran on traditional databases, which works great up to a certain size of data. But when you get to really large datasets, it can have response times that business users are not willing to wait for. What HANA represents is a world class, in-memory database.

We could have attempted to build our own in-memory database, like Tableau and Qlik have tried to do, but it doesn’t solve the real problem – which is, you need to be able to handle terabytes of data and have response times that are less than a second. So when our customers approached us with 20 or 30 terabytes of data, and they also wanted response times at less than a second, there’s only one place really doing that.

The second thing that attracted us to HANA is that we’re a BI company, not a database one. As such, our resources and time is spent ensuring that business users can leverage our tools, rather than have their IT teams run it. Naturally, we wanted to find the best database solution in the market. Tableau and Qlik are great if you want to query on your desktop, something like Excel, but these solutions do not work at the enterprise level.

How will this announcement help differentiate Birst from its competitors?

Ironically, we compete a lot with the data discovery companies like Tableau and Qlik, and this announcement is a big differentiator from them. We have never said that we can build a better in-memory database, and that’s what Qlik and Tableau are saying. What Birst is saying, is that if you want to use in-memory computing, then use the best one out there in the market. Don’t use a desktop version like Qlik and Tableau, but one that is enterprise ready. That’s why this announcement is a big deal for the BI market.

How does Birst’s approach to cloud BI fit with SAP HANA?

In the BI world, what usually happens is that people getting started with the HANA world will try HANA 1.0, which is only available in Amazon Web Services. So, you go to Amazon and you buy HANA by the hour. You can also deploy Birst Instance in Amazon Web Services as well. With this, you can run HANA and Birst together on demand – so, from this perspective, HANA and Birst is a great marriage.

More to the point, 25 percent of our customers take Birst and deploy it as a virtual appliance – that is, the exact same code base and architecture that is used in our public
cloud can be deployed on a customer’s premises where they can run it on their HANA appliance. This is where Birst shines; large enterprises that want to use HANA in their datacentre can leverage their Birst appliance right there with their HANA appliance.

We’re staying true to the roots of Cloud BI in that it is low management, run by business users, and updated in real-time. Just now, you can run Birst on your data centre.

How can in-memory computing be brought together with BI?

The key to bringing in-memory to BI is three things:

1)   Business users expect Google-like response times. So when you’re talking about terabytes and petabytes of data, there is no other solution than in-memory. The speed of the response time is really the biggest benefit of in-memory.

2)   In-memory has to be manageable by a business user and today this simply is not the case. Unless you’re talking about tools like Qlik or Tableau, which can’t really scale like HANA, you need a tool that can handle large data sets. This is where Birst and in-memory work really well; all of the manageability piece is automated through our architecture.

3)   A recent trend that we have seen is that business users are increasingly uninterested in where their data is coming from. All they really want is to ask business questions. This is why a business layer, or a semantic layer, on top of a traditional database is fantastic. But this layer on top of in-memory gives the business user the flexibility and speed that they need. In-memory alone is fast, of course, but it asks data questions, whereas in-memory with BI asks business questions.

Where is SAP looking to expand the use of HANA and where can BI help this process?

Where HANA has got a lot of traction is in SAP accounts and on SAP-type data. Where HANA has not been as successful is data outside of that world. HANA is so much stronger than any other in-memory BI solutions out there – with Tableau and Qlik, they can handle about 100-200gb if you’re lucky. With HANA, it’s only getting warmed up on these numbers and really you can throw terabytes at it and it doesn’t even flinch.

When you think about what the power of BI is, it really is the 90% of data you’ve produced in the past year and it’s not simply in your corporate warehouse. But what are you going to do with this data?

What you need is something that’s fast, low management, low overhead and has response times with sub-second queries. There’s only one solution for this, from a database perspective, which is HANA. Birst is the only BI tool which can be on top of it. When you put Birst on top of HANA and you think about it, really this is the only way to get HANA out into the business users’ hands; no business user can say that they can run HANA, but every business user can say that they can run Birst.

Now you’ve got HANA out into the hands of the world, you can begin to analyse SalesForce data, Marketo data, and social media. You don’t have to wait for the people in IT who are up to their necks with SAP support requests to deliver results.

Is The Price of HANA An Issue?

A doubter would say that it’s the best out there but it’s too expensive. And that’s true; it’s by no means cheap. What I would say, however, is that HANA 1 is fairly affordable. And there is an approach that could give you the best of both worlds, which is storage of your data in an insanely cheap disk-based solution like Amazon Redshift, combined with the pooling of aggregates or your most common queries into an SAP HANA 1 in-memory solution. What you have here is an affordable in-memory solution with an incredibly cheap way of storing terabytes of data, yet you still get response times of sub-second. That’s what we can do, and no one else is capable of this.

This ‘multitude of databases’ is something I think will become more popular in the BI world over the next 6-12 months. Some people won’t necessarily be able to afford to run HANA on terabytes of data but that type of mixture of storage on disk at really cheap rates with the speed of in-memory computing, will start to evolve as a desired or best practice in BI database design.

This interview was conducted by Furhaad Shah. Furhaad is an Editor at Dataconomy focusing on Business Intelligence, Analytics and Data Security. You can contact him here: furhaad@dataconomy.ru

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SAN FRANCISCO — June 17, 2014 — Birst, the leader in Cloud BI, today announced Birst for SAP HANA to empower organizations to take advantage of Birst’s enterprise-caliber analytics capabilities on top of the latest in-memory technology from SAP. By combining the speed of HANA and the analytic horsepower of Birst, customers can analyze more data in more detail and do so faster than ever before.

Birst is providing a new native cloud alternative for SAP customers who have previously had to choose between aging legacy BI tools that take months to implement and require large teams to support, and limited data discovery tools that are restricted to analysts and create information anarchy with silos of analysis. With Birst for SAP HANA, everyday business users have the power to run robust analytics, including visualizations, dashboards and reports, on the fastest database available today. Paired with HANA, organizations can take advantage of Birst’s innovative Cloud BI platform to easily analyze huge data sets with sub-second response times.

“Birst and HANA are exceptionally well suited for each other. Birst is the only enterprise caliber BI solution designed with the agility that HANAmakes possible,” said Birst Co-Founder and Chief Product Officer Brad Peters. “Legacy BI is so brittle and cumbersome that much of HANA’s value is lost, and desktop discovery tools with their non-relational databases, can’t scale to the same types of problems that HANA and Birst can handle together. It’s truly a unique combination in the marketplace.”

Birst is the first and only BI platform that automates the building of a data warehouse within HANA, dramatically speeding up the time it takes to derive the benefits from in-memory performance. With Birst, SAP customers can access their existing HANA data live and combine it with their other data sources. By leveraging Birst, users can now can make real-time decisions onissues such as supply chain management, financial risk, and customer engagement. As more companies adopt in-memory computing technologies to handle a greater variety of data use cases, it becomes increasingly important to have an agile BI platform that provides immediate insights.

“In-memory computing technology is increasingly emerging as a key enabler for the digital business by empowering the agility, Web-scale processing and fast decision making needed to respond to the business challenges of the digital era,” wrote Massimo Pezzini, Vice President and Gartner Fellow.[1]

Birst for SAP HANA is now available. To learn more about how users can leverage Birst’s enterprise-caliber BI platform to gain rapid insight from in-memory databases, register here for a live Birst webinar, titled “Think Faster with Birst on SAP HANA,” on Thursday, June 26, 2014 at 10:00 a.m. PDT / 1:00 p.m. EDT, or visit www.birst.com/HANA.

About Birst

Birst is the only enterprise-caliber Business Intelligence platform born in the cloud. Less costly and more agile than Legacy BI and more powerful than Data Discovery,Birst is engineered with an automated data warehouse and rich, visual analytics, to give meaning to data—all types and sizes. Coupled with the agility of the Cloud, Birst gives business teams the ability to solve real problems. Fast.Find out why more than a thousand businesses rely on Birst for their analytic needs. Learn to think fast at www.birst.com and join the conversation @birstbi.

# # #

For moreinformation, contact:

Stacey Burbach

Birst, Inc.

415.310.9767

sburbach@birst.com

Mark Kember / Dawn Flynn

onebite

01635 887696

mark.kember@onebite.co.uk

dawn.flynn@onebite.co.uk

[1] “Cool Vendors in In-Memory Computing Technologies, 2014,”  April 11, 2014, Analyst(s): Massimo Pezzini | Joseph Unsworth | Tim Payne | Michele Reitz |Roxane Edjlali

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