Collecting, stroring, processing, analyzing and making automated decisions using big volumes of data.
Find new business opportunities: smarter business moves, more efficient operations, higher profits, and happier customers.
Access your data fast and in real-time manner in your apps. It can be a challenge when it comes to big data volumes.
Decision-Making Algorithms help automate the processing of significant amounts of data and find the best answers.
Cloud-based solutions like AWS EMR and Azure HDInsigths make it easy to deploy and manage clusters of any size.
Our process starts with identifying available data sources, collecting the data, and storing it into Data Lake. For every specific research task, we transform data and move it from Data Lake into Data Warehouse, where it can be analyzed. Based on the found results, we implement decision-making algorithms. And we move portions of the data in operational databases for real-time access.
We identify all possible data sources and gather data. Assuming significant amounts of the data, the task can be accomplished two ways: by increasing the cost of the system and adding resources using horizontal scaling, and by increasing the complexity of the system and adding effective and inexpensive multithreaded algorithms. Both approaches work great, and the best solution usually is somewhere in the middle.
Results when it's done:
All collected data is stored in Data Lake based on AWS EMR or Azure HDInsights: cloud-based big data solutions from Amazon and Microsoft based on Apache Hadoop big data tool combined with the Spark analytics processing engine. The solution is proven by tech giants to be capable of handling even petabytes of data.
Results when it's done:
The data should be translated into an optimal and useful format before business analysts can effectively analyze it. When we have some specific idea or task, we need to prepare unstructured data from Data Lake for analysis by transforming it into structured and ready-for-analysis data with the help of multiple ETL functions. Structured data is stored into Data Warehouse based on Amazon Redshift or Azure Synapse.
Results when it's done:
Some data, results of the analysis, and statistics need to be accessed in real-time. Data Lake can't provide this functionality because of data size. Data Warehouse can be too slow for that type of task too. In case when we need fast and real-time access to the data, we can deploy special operational databases.
Results when it's done:
Quite often, you may need to automate some of your business processes based on your data analysis results. The results can be huge and impossible to process by a group of people in an acceptable period of time, especially when there are multiple criteria involved in the decision making process. Decision-making algorithms help to process the results fast and automate the decision making process.
Use cases:
We avoid On-Premise Big Data Clusters when it's possible as very complex, difficult to scale, and costly to maintain solution. We work with Amazon AWS and Microsoft Azure cloud computing platforms when we deploy our clusters to reduce costs using on-demand resources, scale cluster to any size in a matter of minutes or hours, and easily maintain the cluster with the help of built-in cloud-based features.
