Impetus Technologies

Migrating a company’s data center to an IaaS platform has multiple benefits, from increased productivity to better agility, efficient work, and decreased costs. However, moving data to the cloud can be a daunting task for small businesses. Here we list down foursteps that guarantee an easy and successful workload migration to Azure:     Discover : This phase involves identifying all the existing applications and workloads used in the business’s infrastructure. It can be a long and tedious process but is very critical for a successful migration. When identifying the workloads in the preparation phase, it is best to review the virtual networks and storage solutions. Analyze on-premises workloads in the virtual networks and compare them with the resources in Azure. While doing this, make sure you address all the networking requirements. Remember, purchasing new storage whenever you reach capacity can be a problem. You can, however, consider one of the two types of stora...

Enterprises are storing structured, semi-structured, and unstructured enterprise data in robust cloud-based data lakes to reduce infrastructure costs, while scaling-up with ease. However, cloud-based enterprise data lake has its challenges, which enterprises can address by adopting the best practices in data lake management.   Aligning enterprise goals with data lakes Enterprises that align data lake creation with key business objectives can benefit from enhanced productivity and increased connectivity that data lakes provide. It is important to develop a comprehensive proof of concept for your enterprise and leverage data lakes for specific applications to acquire maximum enterprise insights. For example, banking institutions complying with global data management standards (GDPR, PCI DSS, etc.) need to ensure that their data lakes have the right controls for regular analysis and reporting. It is critical to understand the needs of the bank’s managers, project heads, chief i...

  With the amount of data doubling in size each year, any organization’s biggest challenge is to manage, store, ingest, analyze, transform, and process massive data sets. Initiating a successful big data journey might appear tough, considering the increasing number of new data sources, improved processing capacity, and demand for fresher data. Organizations need to overcome all these challenges to reach maximum operational efficiency and drive business growth. In recent times, many organizations have started investing in the development of enterprise data warehouses (EDW) that serves as the central data system for reporting ETL offload processes. It ingests data from many different databases and other sources both inside and outside the enterprise. Since there is a constant increase in the velocity, volume, and variety of data, already clumsy and expensive EDWs are getting overloaded with data. In addition to this, traditional ETL tools are incapable of handling all the generate...