EPA Elastic Platform for Big Data Analytics

Posted by David Smith on September 16th, 2019

The data-driven titans like Google, Amazon, Apple, Facebook, and Microsoft are the five most highly valued firms in the world today. As organizations work to discover and perceive the value of data from its raw form, many now strive for a more competent and agile analytic system to deliver error-free efficient results. With the advent of cheap and limitless access to new technologies, almost a trillion metric data points are generated outside of data centers. 

The size and forms of data keep on growing with clickstream, sensor, social, mobile, and machine data. This large amount of different types of structured and unstructured data, or big data, if used wisely and innovatively can provide I high value and growth. Studies are being done by big data services  to find innovative ways to capture and analyze these data to create new services in that area. 

Businesses are accelerating their analytics functions to use real-time data to explore new areas like recommendation engines and marketing analytics. But when it comes to large volumes of real-time or near real-time data, Big Data solution providers are finding it challenging to harness authentic data as the volume of data keeps growing. To attain this, there is a need for a state of the art data pipeline system. 

In the current usage or the traditional solutions, there are a lot of frameworks that are very efficient for data analytics, but when the scale of data volume is increased these frameworks lack the capacity for handling with efficiency in both storage and speed of analysis. 

There are several big data solutions and frameworks like SMACK; Spark, Mesos, Akka, Cassandra, and Kafka, that are used in big data analytics solutions. These technologies have varied capacities and storage requirements and other performance and computational parameters. 

While using traditional Hadoop framework, there are various performance challenges regarding the trade-off with computation and storage of the resources. For performing power analytics, machine learning, and statistical analysis on high volumes of data or big data, a highly elastic data platform is required. 

Big data consulting Elasticsearch is an open-source tool that can be used for swift searches that support different data discovery applications newly in use among big data solution providers. Using Elasticsearch significant volumes of data can be stored, searched, and analyzed in near real-time. It is mostly used as the basal search engine that is used to power applications that have both simple as well as complex search features and requirements. 

Thus an elastic data processing and storage system would help to sort this issue of data service and management for big data service companies. Elastic Platform for Analytics (EPA) is designed as an extensible infrastructural foundation to explore, merge and shape the data, and to gain a better understanding of the underlying data relationships across hundreds or more dimensions of analysis and finally deploy the results in a manner that will support decision making.

Elastic Platform for Big Data Analytics 

The benefits of an elastic design in the data platform are mainly the storage efficiency optimization and performance acceleration for different workloads. In traditional RDBMS and warehouse implementations, if one wants to upgrade the data storage capacity, they also have to purchase additional processing capability as the platform cannot dynamically scale. 

The elastic design of the system provides a better computational power vs. storage scalability and can be scaled horizontally to productively meet the deals and demands and deliver a better quality of service, suiting the changing business needs. It also employs a higher speed of networking than previous generation clusters. Saying this, it is evident that in an elastic platform, we can run multiple workloads on the same cluster, reducing both cost and security complications of storing multiple copies of data on isolated workloads. 

Self-tuning architectures also minimize the need for additional administrative resources. Elastic design also provides the flexibility to reuse computational power of each node as a support to new big data analytic solutions and application requirements. You can also add additional nodes without having to repartition the data. These benefits, when applied on high volumes of data, provide a significant impact on both performance-wise and also speed wise.

An elastic platform for the analysis of large volumes of data, have the advantage of an easy data provisioning and partitioning, at the same time preventing data redundancy and data sprawl. The platform, whilst being compatible with the storage and computational power constraints, also provide the well-required security, enhance the ease of governance and ease of use. 

The platform also includes compatibility for different big data technologies and has dedicated modules for YARN (dependency management system), distributed file system, thereby facilitating smooth interoperability and two-way communication between various organizations working together. This makes the Big Data consultants choose the platform based on their computational and compatibility needs. Thus it is essential to choose a platform that will include the support for loading and fetching queries from independent objects on commercial platforms like Amazon S3 or Azure Data Lake. 

As the platform provides custom on-demand deployability, there is no need for a separate extensive internal IT infrastructure or administrative infrastructure. In case of integration of newer workloads like Deep Learning, the modules can be easily integrated into existing infrastructure by merely adding additional relevant EPA blocks. 

This ensures cost-effectiveness in general usage and provides a better density, usage efficiency through workload optimized components of the platform. The EPA platform allocates a tailored work environment that has different performance variables like different workloads or high computational power for different users in an isolated environment, thereby assuring the quality of service. 

The database on the cloud will ensure an on-premise model with a hybrid solution in data usage and storage functions. With quick integration capabilities, the analytic platform is better accessible hence making the process of data sharing for the data engineers and result in analysis easier for developers.