Impetus Technologies

Data lake refers to a repository that captures, stores, and manages raw, detailed data in its original schema or format. Detailed source data is the focus of Data Lake, which can be repurposed to meet new requirements as advanced analytics emerge and evolve. In a way, data lake future proofs analytics by provisioning ample source data for a wide range of analytics. As data volumes today are exploding, it becomes important for a data lake to scale to hundreds of terabytes or petabytes. For enabling massive scalability at an economical cost, Hadoop has emerged as the most preferred data platform for data lakes. Besides Hadoop, other similar data-driven patterns, such as enterprise data hubs or data vaults, may be deployed on RDBMSs (relational database management systems). How Data Lakes and Data Warehouses Can Work Together? A Hadoop data lake can extend the capabilities and life of a data warehouse to a considerable extent. In the present hybrid environment, the core wareho...

The legacy data warehouses come with a host of limitations, propelling enterprises to look forward to advanced methodologies. With the advent of cloud-based data warehouses, scalability and performance issues   are   significantly resolved, leading to more fruitful outcomes. One of the most sought-after cloud-based data warehouses, Snowflake, has been substantially contributing to the improved scenarios. With advantages like complete SQL support alongside serverless framework and strengthened connection with Business Intelligence and various ETL tools , Snowflake has been at the forefront of the business digital transformation. No matter what the book says, the migration from a traditional data warehouse to a cloud-based warehouse, prominently Snowflake, poses a wide variety of challenges including progression of numerous ETL/ELT jobs, redundancy of the legacy environment, difficulty in the recognition of optimal components of the cloud architecture, data validation,...

As businesses expand, there are multiple challenges that the existing enterprise data warehouses begin to experience. Storing and synthesizing of large volumes of data are among the key challenges faced. Legacy enterprise data lake lacks the flexibility and scalability required to make accurate and timely business decisions.  A single source of truth is the answer to these challenges. Building a unified data model helps an organization to create a holistic view of their data that can be utilized across the enterprise with ease and confidence. Single Source of Truth across the Enterprise: Things To Consider A single source of truth is an end-to-end solution that helps convert your existing data silos to modern data architecture or an on-premise big data warehouse. The solution follows an incremental approach and comprises of five stages – assessment, transformation, preparation, access, and consumption. Each of these stages drives immediate business benefits and offers ...