Most large enterprises leverage traditional data warehouse and online analytical processing (OLAP) to support relational analytical queries over normalized and dimensional models. Over the period of time, they have made significant investments in hardware, software and consulting resources to aggregate data from different silos and data sources. These traditional data warehouses are used to generate reports, gain insights and the clients have grown comfortable latching to those systems, even though they are spending significant amounts of money and many a times they are unable to get timely insights.
Advancements in technology and massively parallel processing capabilities that are now made available through cloud platforms, coupled with dramatic increase in the volume of data, both unstructured and structured being generated are driving enterprises to consider more viable, scalable and cost efficient data management and analytics solutions.
With so many established cloud providers and new entrants in the markets, further fueled by the speed of innovation makes the task of evaluating alternate options even more challenging to many enterprises. Even established big data Hadoop distributors are either developing or have developed services for leading Cloud platform like Microsoft Azure, Amazon AWS and Google Cloud Platform (GCP). Cloud platform through their affordable-cost, pay-as-you-go pricing models, quick provisioning and de-provisioning offer enterprises a platform to pilot and validate concept of operations before considering full scale development and migration.
On one of the projects, Vikheda leveraged Google Cloud Platform features like Big Table and SQL to create storage for data collected. We took advantage of the platform that Google has invested heavily and made full use of its high-speed network, serverless architecture and its compute capabilities to create feature rich and user friendly application and dashboards. Google’s GCP features such as BigQuery, Cloud Data Proc coupled with AI features like TensorFlow make it very attractive for clients consider these solutions.
Microsoft too has comprehensive data management and analytic solutions that are enterprise ready. As a dominant leader in the enterprise space, Microsoft offers several powerful capabilities such as Azure SQL Data Warehouse (a fully managed, MPP cloud data warehouse), Azure HDInsight (a Hadoop distribution based on Hortonworks), Azure Databricks (an Apache Spark-based analytics platform) and Azure Data Lake (a big data store and analytics platform) as cloud services.
Enterprises are now presented with options and capabilities that they never had before to mine their data and extract the wealth of information that is embedded in them. With a sound data strategy and governance model, and with an appetite to innovate in short cycles, they can chart their own success with these amazing cloud platforms that available in the marketplace today.
Data management and data analytics is a core competency of Vikheda and we welcome the opportunity to collaborate with you on your data initiatives. Contact us to get us engaged in your data initiatives.