Complex, true real-time analytics on massive, changing datasets.

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1 Complex, true real-time analytics on massive, changing datasets. A NoSQL, all in-memory enabling platform technology from:

2 Better Questions Come Before Better Answers FinchDB is a NoSQL, all in-memory enabling platform technology from Finch Computing. Part database, part analytics engine, part search tool, FinchDB was built on the belief that better questions have to precede better answers; and that asking better questions should be the bedrock of any analytics initiative. As data volumes grow, as data becomes more complex, and as analytics needs originate from across the business, FinchDB is uniquely suited to help any organization find greater meaning and insight in its informational assets. FinchDB was built from the ground up to be all in-memory for in-memory applications. So, it s incredibly fast. It operates on data that is streaming or static, structured or unstructured, words or numbers, internal or external. FinchDB is also massively scalable. It s a high throughput, distributed system that can operate on commodity hardware on premises or in the cloud. It supports faster, more complex, true real-time analytics, leveraging more than two dozen innovative pieces of intellectual property to do it.

3 Traditionally, businesses have relied on three answers-oriented technologies to leverage their informational assets: databases, search tools and analytics engines. But each has flaws. Three Answer Technologies Fall Short Database Search Technology Analytics Engine Must know which datasets you have, and the data must be cleaned and prepared for use. Must be able to articulate the data you want and trust that it s the right data. And all of it. Must know question(s) to ask; must continually rebuild models; and tolerate suboptimal latency. Our IP Addresses Those Shortcomings Models Embedded in Queries In-Memory Architectures Event Detection Alerting Fuzzy Searching Scored & Ranked Results Co-Occurrence Mining Compression On-the-Fly Linking Topic Modeling Entity Disambiguation Knowledge Discovery So You Can Ask Better Questions All Three. All Together. All In-Memory

4 With FinchDB, You Can Apply predictive models on-the-fly, at the time of transaction, across multiple business moments Perform in-memory analytics, at scale and on commodity hardware Deliver true real-time performance supporting automated decision making Perform per-transaction, predictive analytics Link disparate datasets to find hidden relationships and insights Leverage search, analytics and database capabilities together in one solution

5 Is FinchDB Right for You? How are you asking questions of your data? What NoSQL database platform are you using? And what are you using for analytics? Is it flexible enough, fast enough, responsive enough? What about enterprise search? What type of data do you have? How much do you have? How large are the datasets you re working with? What type of data is it? In what format? Where is it coming from? If streaming, how fast? Are there variances (size, type, format) across the data in the stream? What are you looking for? Batch processing requires knowing the specific question you want to ask, or thing you want to find; do you always know that? Are you looking for precise answers or candidate sets? Would seeing a list of likely answers, scored and ranked, be of value? How are your queries structured? Who informs that process? Do you need flexibility in how you structure queries so that you can better explore your data? What part of the business drives most of the organization s analytics needs? What rules, of what type, do you have in place? Are you analyzing individual transactions or data in the aggregate? Are you analyzing every transaction the same way? Do you have a need to compare real-time data to historic data? Do you change your analytics models? How frequently and how long does it take? Who develops the models? What about speed, volume and outputs? How many transactions are you attempting, and in what timeframe? What types of response times are you getting? What is the goal? What do you do with the outputs? How are you using them? What are you using to store your data? Are storage costs, storage footprint or hardware a concern? Is data compression a concern? Do you use any in-memory solutions currently?

6 Finch Computing, formerly Synthos Technologies, is a division of Qbase, LLC. Together, we build and support new ways of interacting with information. Learn more: Washington, DC Sunrise Valley Drive Suite 300 Reston, VA toll free San Francisco 28 Second Street Floor 3 San Francisco, CA Beavercreek, OH 3800 Pentagon Boulevard Suite 110 Beavercreek, OH

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