Document Repository - A Big Data Processing Engine
A scalable solution for a document repository with a data processing engine. This case study outlines the architecture that was implemented to address the requirements of a government department that handles a large number of correspondence documents that need to be archived.
How we Built a Powerful Retail Analytics Engine for 30TB Big Data Churns
Follow the journey of a Pittsburgh based analytics firm that went from data chaos to a highly concurrent, elastic, non-blocking, and asynchronous data architecture and save over 22 hours of run time while processing 4.6 billion events.
Data Engineering has become increasingly relevant in the highly-connected, AI driven world. Today, Data architectures are as vast and varied as the use-cases they support. For most of us, Data architecture simply meant running an RDBMS for all of our needs, from transactional read-write workloads to ad-hoc point and scan analytics loads. As Data grew in numbers more use-cases for Data-driven products such as fraud detection systems, recommender systems, personalization services came into existence.
Data engineers entered the field to solve our problems by introducing specialized Data stores for example; search engines, graph engines, large scale Data processing such as Spark, NoSQL, stream processing and the machinery to glue them together like ETL pipelines, Kafka, Sqoop, Flume.
Internet of Things
The Internet of Things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers (UIDs) and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. Emerging trends in IoT include big data convergence, data processing with edge computing, Auto-ML (Machine Learning) for data security, blockchain and more.
Build your data architecture rapidly for higher business benefits
The next frontier of competitive advantage is going to be the speed with which you can extract value from data and derive business benefits from it. So, the innovation in the data age requires advanced performance with the help of AI, machine learning and data science. Know more on the unification between microservices and fast data architecture, and the tools and technology behind the fast data architecture.
Extremely motivated UI/UX designer with 4 years' multiple working backgrounds, enabling me to acquire various
experiences for resolving problems. Took charge of generating responsive web platforms and mobile applications that
successfully contributed to iterate the usabilities and the journey of the products. Self-taught programming abilities
for better collaboration and practical capability.
Robie has seventeen (17) years of experience in Talent Acquisition, Human Resources, and Business Development.
She is handling full cycle in Operation Management, end-to-end Recruitment both IT and NON-IT all across levels (from Account coordination up to job offer), Compensation and Benefits, Payroll, and Administration.
Point of contact for IBM (Solutions Delivery, Business Services, and the Philippines) and the other Business Partners from different Industries. She has experience sourcing and processing candidates for EMEA, APAC, UK, USA, and Australia.
Ashutosh (Ash) has over 20 years experience in the technology industry with customers ranging from start-ups to large multinationals – in a wide range of industries including High tech, Engineering, Software, Insurance, Banking, Chemicals, Pharmaceuticals, Healthcare, Media and Entertainment. Experienced in leading and managing cross-functional teams through the entire product development life cycle.
Skilled in emerging technologies, software architectures, framework design and Agile process definition. Implemented enterprise solutions as well as commercial products in domains ranging from Big Data, Business Intelligence, Graphics and Image Processing, Sound and Video Processing, Advanced Text Search and Analytics.
Unified Data Repository & Analytics for Enhanced Risk Assessment
Growing volumes of structured and unstructured data relating to claims and lack of any capability in processing or driving business outcomes from them was becoming a business and technology challenge.