Big Data
To understand the real potential of big data one needs to understand how different is big data technology as compared to conventional technology, then map those differences to measurable benefits. At Accion Innovation Center we implement a big data pipeline, to get various technology components work together. Most of these components are available as open source but they need to be integrated together into a seamless framework to allow easy implementation of the big data pipeline.
Shishir Shrivastava
Experience on hybrid mobility, web frontend and backend frameworks, IoT, chatbots, big data and blockchain and open-source tools.
Dwaip Chowdhury (DC)
Dwaip Chowdhury is a passionate Enterprise Architect/Engineer devoted in architecting Enterprise wide distributed cloud first products, Big Data inspired solutions while enabling others to perform their roles more effectively. At Accion Labs, he oversees the design and architecture of a number of client projects, focuses on digital transformation technologies such as ML, AI etc.
Dwaip has designed and built horizontally scalable products based on microservices architecture and polyglot persistence with AWS, Docker platform, and DBAS; distributed RESTful API services; large Big Data solutions; and web-based front-ends with Angular 6.0. He earned his BE Degree in Mechanical Engineering from Jadavpur University, Kolkata.
Mithun Urs N
7+ years of Industry experience with a demonstrated history of working in the Automotive & Betting industry. Skilled in Big Data development (Talend), Oracle Data Integrator (ODI), Linux, Microsoft Office, and PL/SQL. Strong engineering professional with a Master of Computer Applications (MCA) focused in Computer Science.
How big data is improving clinical outcomes
From banking to retail, many sectors have already embraced big data, regardless of whether the information comes from private or public sources. Traditionally, the healthcare sector has lagged behind other industries in the use of big data.
Abhishek Singh
Over 1.4 years of Total IT professional experience in Big data and data warehousing (ETL/ELT) technologies
includes requirements gathering, data analysis, design, development, system integration testing, deployments
and documentation.
● Research & develop Machine Learning models for Defense.
● Research Models for human detection in any climate circumstances.
● Provide SW specifications, production quality code and engage with algorithm proliferation activities
● Research and develop machine learning algorithms for drone night vision human detection.
● Hands on experience in solutions for Big data using Hadoop, HDFS, Map Reduce, Spark, PIG, Hive, Kafka, Sqoop,
Zoo keeper, Flume, Oozie.
● Excellent knowledge and hands on experience of Hadoop architecture and various components such as HDFS, Job
Tracker, Task Tracker, Name Node, data Node and Mapreduce programming paradigm and monitoring systems.
● Hands on experience in installing, configuring, and using Hadoop ecosystem components and management.
● Experience in importing and exporting data using Sqoop from HDFS/Hive to Relational database Systems and
vice - versa.
● Experienced and well versed in writing and using UDFs in both Hive and PIG using scala
● Excellent understanding with different storage concepts like block storage, object storage, column storage,
compression storage.
● Extensive experience in Extraction, Transformation <<>> Loading (ETL and ELT) data from various sources into
data Warehouses and data marts with industry best practices.
● Experience with Informatica ETL for data movement, applying data transformations and data loads.
● Good working experience with different Relational DB systems.
● Very good understanding with implementations in building data warehousing and data marts with OLTP vs OLAP,
star vs snowflake schema, normalization vs denormalization methods.
● Hands on experience in building wrapper shell scripts and analysis shell commands in practice.
● Supported various reporting teams and experience with data visualization tool Tableau.
● Very good at SQL, data analysis, unit testing, debugging data quality issues.
Siva Kalyan Karpurapu
A Certified Informatica Developer, Certified Data warehousing specialist, Informatica, Datastage, OBIEE, Pentaho BI etc.
Accion Labs One Of 10 Most Promising Big Data Companies – CIO Review Magazine
We are happy to announce that Accion Labs’ big data division in India – Reach1to1 Technologies, has been selected as one of the 10 most promising big data companies by CIO Review Magazine.
Tony Kernan
Tony has over 21 years of IT sales and client management experience. He has an exceptional understanding of the factors governing a successful client-supplier relationship and world-class experience building outcome-oriented client engagement models. At Accion Labs, he leads the company's global Business Development and Engagement strategies, helping clients develop innovative IT solutions in emerging tech space including AI, ML, Cloud, BIg Data, BI/DW, Open Source, Web 2.0, MoBIlity and Social Media.
Tony’s vision is to help Product and Tech Entrepreneurs anywhere and everywhere along the Product Development Life Cycle through innovative adoption of Digital Transformation, Machine Learning, and Automation best practices. He is a graduate of Duquesne University, a lifelong resident of Pittsburgh, and has been active in coaching, mentoring, and teaching throughout his career.
Performing Big Data Analytics using Apache Spark for .NET
big data is not a cult. We are living on the verge of a revolution that is touching every industry, business, and life on this planet. With millions of tweets, iMessage’s, Live streams, Facebook, and Instagram posts… terabytes and petabytes of data are being generated every second. And getting meaningful insight from this data is quite a challenge since the traditional databases and data warehouses are not able to handle the processing demands of these big data sets. One of the reasons is that these data sets need to be updated frequently and often in real-time as in case of stocks, application performance monitoring or user’s online activities. In response to the growing demand for tools and technologies for big data analytics, many organizations turned to NoSQL databases and Hadoop along with some of its companions’ analytics tools including YARN, MapReduce, Spark, Hive, Kafka, etc.