What is Big Data?
People have been using the term “big data” since the 1990s. Originally, the term meant “huge data sets that cannot fit on a single machine and be processed in a reasonable amount of time.” Since then, the term has undergone some revision to adapt to technology changes. Nowadays big data essentially means,
“Huge sets of structured, semi-structured, and unstructured data that can be mined for insights that drive strategic business decisions.”
In other words, big data is exactly what it sounds like: massive amounts of data.
Data Engineering Solutions
Our data engineering solutions are designed to support all data-driven processes. From optimising your data warehouse to align to your business goals, through to data migration and consolidation, we will always aim for improved business agility and adaptability.
Raw data from different sources, when presented in verified and ready-to-use forms, can be managed and processed into real-time business-critical information. Our team provides ETL system development services. With your business goals and strategies in mind, we are ready to assist you in converting raw data into valuable insights that you can use to derive new business value.
ProGineer’s Big Data Solutions
Since 2010, ProGineer Technologies has been designing and implementing big data solutions. As the main development center for some multinational companies, we have been given some unique challenges. For example, we were asked to design solutions with the ability to process huge amounts of data produced at an extremely high rate by state-of-the-art manufacturing equipment in the semiconductor world. Our solution can run on thousands of pieces of equipment and collect data at high frequencies of one millisecond per measurement! The system can:
• Process and store data
• Resolve inconsistencies
• Detect excursions
• Generate alarms
• Summarize the data at various levels of abstractions
If any of these functions are delayed, your entire production yield is at risk. Traditional database solutions failed miserably when they tried to process this large volume of information. Relational databases on their own could not scale up. But ProGineer was able to create a new solution up for the challenge!
The engineers at ProGinneer designed hybrid systems based on relational databases using Oracle and NoSQL databases using Spark/Cassandra. This innovative design was able to flawlessly process this flow of data. The data is stored as a temporary repository until the more time-consuming processes like excursion monitoring, thumbnail image generation, summarization, correlation, and clustering are finished. Once these processes are complete, the results are pushed into dedicated relational databases for later reporting and ad-hoc analysis.
The Role of Automation Classification
We also have the option of enabling automation classification for incoming data. We do this using Spatial Signature Analysis algorithms to flag devices that match historical trends. Not only does this allows us to eliminate any potentially bad devices in real time but it also reduces costs significantly.