Seth Rao, CEOEnterprises are adopting Big Data solutions to streamline a variety of operational processes, like marketing, compliance, analytics, customer and transaction management, etc. They are moving from the slower batch-mode Big Data processing (using MapReduce framework) to real-time Big Data processing (with Spark, an open source cluster computing framework), enabling them to leverage Big Data in real-time operational decisions. With the increasing use of high speed, large volume, and complex data in a variety of formats, ensuring data is fit-for-use (i.e., of right Data Quality) has become more important than ever. “In the absence of a scalable, automated solution to detect Data Quality issues, companies are not only risking returns on their Big Data investments but also exposing themselves to legal, regulatory and PR/brand risks by using potentially unverified and incorrect data,” says Seth Rao, CEO, FirstEigen.
Headquartered in Greater Chicago, Illinois, FirstEigen, a Big Data Reconciliation/Validation and Analytics company provides cross-platform data validation software to detect errors in large databases and data flows. It ensures sanctity of data at the point of use. Their software DataBuck is a plug and play tool that allows non-technical users to validate large volume of data at rest or in motion, across multiple platforms and set up repeatable and auditable checks themselves. It performs Data Matching/ Validation, anomaly detection, Big Data Profiling, and Data Quality checks. It is architected on Spark and leverages its massive parallel processing power combined with proprietary algorithms to deliver 10 times faster performance than any other traditional approach. Its architecture is linearly scalable and does not get bogged down even when processing massive volumes of data. In contrast, conventional Data Validation tools rely on client server architecture (a linear computational engine) and are limited in their ability to process large volumes of data even at moderate speeds. “DataBuck is packed with an array of enterprise-class features to look at Big Data at rest and in motion,” says Rao.
Data Validation is the connective tissue that can authenticate the trustworthiness of all data in an organization’s systems.
DataBuck is an ulta-fast, cross platform Big-Data Reconciliation and Validation tool that can be delivered either via cloud or on-premise
The Big Data environment is complicated with multiple technologies and multiple layers, which can take significant resources and coding over weeks to connect. DataBuck’s code-free interface enables even beginners to setup data validation rules and match disparate large data sets in less than 15 minutes. In addition to freeing up resources it standardizes the process and minimizes in advertent errors creeping in from constant manual coding/scripting. It can sit on top of Data Lakes or Data Reservoirs and can effortlessly validate incoming data without coding. DataBuck also comes with several industry standard algorithms out of the box that can capture a majority of anomalies in financial transactions.
DataBuck has partnered with Cloudera, MapR, HortonWorks, MongoDB and others to offer a purpose-built Big Data validation solution. It natively connects with their Big Data partner’s platforms and those of others like Vertica, Teradata, Apache Hadoop, and cloud platforms like Amazon AWS and Microsoft Azure. “DataBuck has a hybrid approach to big data validation, both via cloud and on-premise installations,” says Rao.
DataBuck’s pre-built connectors can read from most common big data platforms with no programming needed from the user. To ensure code-free integration, the company plans on releasing more connectors to integrate with the ever-increasing Big Data platforms. “Our goal is to deliver absolutely, positively trustworthy Big Data, without coding,” concludes Rao.