The world’s data now doubles in volume every two years! We’re all living in a data-governed age where business doesn’t just run on data, business is data. Fueled mainly by factors like the rapid growth of worldwide web, e-commerce, the mobile revolution, the rapid growth of social networks, cloud computing and Internet of Things (IoT) among others. In this era, our lives are constantly in the “always-on” mode and “always-connected” with easy access to rich sources of analyzed information coming from the Internet, smart devices, servers, machines, sensors, and so on. Before this data-centric world, we lived in a structured world of RDBMS and ERP soon followed by the emergence WEB with HTTP and Internet which was accompanied with the client server programming. With the emergence of Apache Hadoop, we entered a new Data Era with the need to handle phenomenal levels of data with respect to variety, volume, velocity and vectors of data. Based on analysts’ projections, we will soon be witnessing an avalanche of data with with cloud strategy playing a major role in the enterprise data ecosystem.


Needless to say we now face a major paradigm shift in the data industry, with data being captured and loaded from various sources like Twitter, Facebook, LinkedIn, IoT sensors, smart devices, SaaS, cloud and enterprise data whereby, all this data would reside in diversified locations, within Premises infrastructure, externally hosted repositories or in the Cloud.  Also, there is a shift occurring on how the data is being stored and analyzed.  It is expected that the complexity and the amount of data will continue grow exponentially. The increase in adoption of cloud storage, especially for capturing big data will add to this ever increasing complexity. Looking at all this, the complexity factor is now a real challenge and the idea that data is all going in one central data store is far from reality. Like it or not, for many, the so-called ‘data lake’ is distributed across many data stores. EMC predicts that digital universe will grow 10X – from 4.4 trillion gigabytes to 44 trillion by 2020.


Another important fact to note here is that 80% of the work that data scientist put into big data projects is spent on data integration and resolving data quality issues. Data integration is a key component of the Hadoop solution architecture. The traditional systems have been under pressures of managing the challenges of the new requirements that include scalability, while being cost effective. Additionally, there is a strong need for reusability which would entail and a define once, execute anywhere philosophy. Also, the ability to be service oriented i.e. Data integration as a Service. Last but not the least, being a data sponge is also imperative; here the aim is to integrate multiple data types in-motion and at rest, and on demand service: In short, “Data integration on-demand”. Today, we have point solutions which do not scale to meet the modern data challenges:




At Diyotta we have conquered these challenges.




Diyotta transforms Hadoop into an information hub by modernizing the approach to data integration. It is the industry’s first modern data integration platform, purpose-built for big data, hybrid data systems, and the Cloud. We speed up value creation for big data platforms like Hadoop and accelerate the data warehouse modernization, by moving data from Cloud to On-Premise; from On-Premise to Cloud, and also loading data from emerging data sources like Twitter to Hadoop, and from Facebook to Hadoop. We enable Flexible configuration of ELT processing, along with the exploitation of scalable platforms to transform big data, leverage the power of Hadoop, Spark and MPP RDBMSs, and not to forget, enabling operations and orchestration for the enterprise-ready production implementations.


Diyotta recently introduced Modern Data Integration Platform as a Service (iPaaS) that fully leverages its proven architecture for data integration. Diyotta not only exploits the power of scalable platforms to process data at scale, it can do so while also providing the ability to execute filters, data cleansing, and transformations across multiple platforms as part of the same design flow. Also, Diyotta moves data  directly from source to target points thereby reducing network bottlenecks and optimizing the data movement throughput. Diyotta’s flexible architecture enables data architects to design data integration jobs configuring them to execute in a centralized or distributed manner.


Keeping in mind all the challenges we are exposed to at present and will be expecting in the tsunami of data in the days to come, the smart way to get the optimized performance in a distributed data environment is to take integration to the data and not the other way round as traditionally being done in the past. Therefore, Diyotta offers the answer to deal with these new set of requirements, address the data deluge, unify data integration siloes and allow companies to remain agile in a distributed data landscape.