For a general software application developer, working with heterogeneous devices can be very intimidating. It not only requires understanding of device internals but also how to synthesize your application to the target hardware. Software application needs to be written in a way such that it is amenable for building hardware. An optimized software will not always result in optimized hardware. At BigZetta, we have a team of hardware experts who know how to take an application and convert it to optimal underlying hardware. Our experts understand FPGA, High Level Synthesis and backend tools in intimate detail. Whether you application needs to run on cloud or on locally installed hardware, we have the expertise to build and deploy for both choices.

Modern data centers provide a choice of heterogeneous hardware to pick from (with varying cost-speed trade-offs). However, most of the existing applications have not been designed to benefit from this heterogeneity. There is a widespread lack of knowledge on how to build and deploy for heterogeneous compute resources. At BigZetta, we have developed in-depth expertise to take an application and modify it to work on heterogeneous hardware in a fast, robust and scalable way. Design of such application not only requires knowledge of heterogeneous devices but also a careful partitioning and mapping of system to right resources.

We understand what it takes to solve a problem involving terabytes of data optimally. Careful design of clusters and setting of tuning parameters are super important to extract the maximum juice out of a big data application. Our expertise with these tools enables us to help our customers in deploying and running popular big data tools in an optimal and cost-effective way. Whether it is setting of right configurations for your query engines (like Hive), or enabling LLAP functionality for BI queries, we can optimize the query performance across a wide range of inputs. Choice of right backend (MR, Tez, Spark etc.) can dramatically impact the query performance and a sub-optimal choice can lead to degraded performance and increased operational costs.