Impetus Technologies

  Snowflake is a cloud data warehousing platform that makes it easier for the data team to use and store data. Contrary to traditional storage solutions, there are various data types and business intelligence tools supported by Snowflake. This makes collaboration within the internal and external teams easier throughout the  ETL data migration  pipeline. Snowflake also supports most of the structured and unstructured data types. While many customers are excited at the prospect of migration, they lack the knowledge of how to start. Regardless of where you are starting from, here are a few considerations to make while moving to the Snowflake.   1. Say goodbye to partitions and indexes Contrary to other data warehouses, Snowflake doesn’t support indexes or partitions. Snowflake rather focuses on the automatic division of large tables into micro-partitions used to calculate statistics pertaining to value ranges carried by each column. These insights are then used to...

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...

Developing comprehensive data lake architecture is one of the best ways to enhance operational efficiencies while having a unified source of data truth. Firms can analyze customer information in real-time, extract meaningful trends & insights, and remain compliant to industry norms/regulations in a highly robust manner. As it relates to data lake implementation, preserving data integrity in its native form is critical. Additionally, it is important to handle data sources correctly while ensuring that sensitive information is protected when accessed or stored. There are several bottlenecks that enterprises must address, which is why following the four strategies below is critical to implementing a robust data lake successfully.   Outlining business requirements early While data lakes are designed to hold large structured, semi-structured, and unstructured data quantities, it is best to create a data lake that fits   your business requirements. Enterprises need to ana...

  In today’s fast-paced world, enterprises consider data migration from legacy to modern systems to improve productivity and efficiency. However, abandoning a system and moving onto another involves many challenges like old data retention, adjusting the new system with the existing ecosystem, etc. With cloud migration services , multiple data sources can be consolidated into one and made the source of truth for all terms. By the time an organization decides to begin its data migration journey, it already has clear goals and a tentative budget in mind. However, the real challenge lies in choosing the apt migration solution. Some factors that need to be considered when choosing a migration solution are: Size of Data That Needs To Be Migrated: Only those organizations that want to migrate a small amount of data that doesn’t impact the decision making can opt for manual migration. However, when legacy, data context, comments, and attachments are crucial for business, it is best to ...