The topic of Big Data Analytics is not new one. Big Data Analytics has formed the backbone of major eCommerce success and it has been adopted by government agencies as a part of their digital government initiatives. The questions that should be asked is “What makes Big Data and why we need it?” and “What should data Analytics be focused on?” This technology has moved beyond what is it and Big Data has become a prerequisite to enable AI/ML to provide services such as cyber protection, and for intelligent automation.
Big Data projects do not have to break the bank if done right. This is where Satsyil is different from those large or billion-dollar IT firms, we have proven experience. At the heart of Big Data – we start from evaluation and implementation of the architecture, ecosystem, and Infrastructure. Big Data can uncover valuable/insights information from the enormous amount of institution data ingested into Data Lake/repository. Using wide range of tools such as Python, Spark, Pig, YARN, HIVE and Hbase, we can transform structured and unstructured data for batch/stream processing, APIs, searchable indexes, and data Analytics.
Data Analytics is made possible with an abundance of tools such as Tableau, Databricks, and AWS Analytics services (i.e., Kinesis, Elasticsearch) that make use of Big Data repositories. As more and more companies are building Analytics tools, some of the pioneer companies are becoming well known with their technologies, such as Databricks and Snowflake that created a unified Analytics platform in helping build data warehouses on the cloud for Data Analytics and Data Visualization purposes. For example, Databricks provides the ability for developing scalable AI projects to process large amounts of data reliably and Snowflake provides elasticity of a cloud-based data warehouse. Satsyil has experience in building Data Analytics projects using thousands of terabytes repositories; we have incorporated tools such as Databricks, Snowflake, Tableau, Kafka, AWS, Azure, along with implementing AI Tools (e.g., TensorFlow). We have deployed Big Data & Analytics for data mining (of a large dataset to identify patterns and relationships), Deep Learning and Predictive Analytics.