Datameer
Transformational Approach to Data Analytics

Since last decade, Hadoop has successfully dealt with distributed processing of very large data sets on computer clusters built from commodity hardware. However, many organizations are trying—albeit unsuccessfully to utilize their data capabilities and enhance their effectiveness with scalability of big data streams, cost effectiveness of a data platform, software flexibility, and fault tolerance. Nullifying these pain-points is Datameer; an analytics company headquartered in San Francisco, CA. Datameer utilizes Hadoop’s unrestricted storage and compute power with a common spreadsheet interface and dominant functionality accelerating businesses towards agility and operational efficiency.

“Datameer has made big data analytics easy for everyone. We believe that complex challenging issues can be solved with data which is why we place the power of big data analytics directly to those who know their data best,” says Stefan Groschupf, CEO of Datameer. “While other big data analytics tools just connect and copy data from Hadoop, Datameer is built from the ground up to take advantage of the unique analytics and schema-on-read processing power of Hadoop,” says Groschupf. Additionally Datameer also shares clients’ Hadoop environment with other ecosystem components in the organization, thus interoperating with the full platform and its native APIs. Datameer is capable of Big Data integration and preparation. Additionally Datameer also performs big data analytics, visualization, and provides advanced data modules.

Datameer’s big data architecture is built on top of a thin micro kernel foundation, along with a collection of modules delivering unique functionalities. This makes the platform extremely flexible, extendable, and robust. The data integration is seamless, with all forms of data—big, small, structured, or unstructured— integrated seamlessly without the requirement of schemas. While in traditional ETL (Extract, Transform, Load), data preparation is a part of the assembly line Datameer keeps the preparation and analysis as iterative and complimentary.

We place the power of big data analytics directly to those who know their data best

This means the client’s data preparation tasks are driven by their analyses, and vice versa.

Datameer has aided various organizations to utilize their data effectively. A case-in-point would be Yapi Kredi, the national private bank of Turkey. With over 19,500 employees and 11 million active customers, Yapi Kredi is the fourth largest bank in Turkey. The challenge that Yapi Kredi faced was the immense volume of data with which value was to be derived—increasing business agility, reducing operating expenses, and improving the overall customer experience. Yapi Kredi partnered with Datameer, to update its credit risk score model. It was built on SAS along with traditional business intelligence tools— Informatica for ETL, SAP IQ for data warehouse, and Business Objects for reporting. With the help of Datameer, Yapi Kredi streamlined and shortened the credit risk model update process. Datameer simplified the preparation necessary for the data scientists to update their models in SAS, and included business analysts in the process to determine the attributes in the model that need to be updated. The results were that Yapi Kredi could now extend big data beyond the data scientists to the business analysts for faster business insights, helping data scientists to be able to prepare their data for credit risk model in SAS.

At Datameer, the emphasis is on listening to customer’s problems and finding unique solutions. Groschupf believes that a lot goes into creating a company that endures. “We will continue our quest to democratize big data analytics and bring analytics even closer to decision makers,” concludes Groschupf