Business Intelligence in an era of Cloud Platforms, Data Science Algorithms, and Multiple Integration Patterns

Right now, enterprises are all about data. The companies that can leverage data effectively are having a winning edge. Data is a strategic asset. Data drives which products to choose, which business models to pursue, and which customer experiences to create. With ever increasing complex business requirements with a mushroom of tools, frameworks and platforms, the universe of business intelligence is no longer restricted to ERP and CRM insights coming out of simple data warehouse.

SnowPipe – Serverless data ingestion from Azure to Snowflake

Snowflake offers a number of ways to ingest the data starting from batch processing to stream processing. SnowPipe feature offers micro-batch processing capability and is one of the very powerful means of ingesting your data without having to deal with provisioning of compute resources. Once configured and used for the right use cases, it offers scalable, cost-optimized and serverless means of loading your files from Azure's storage into snowflake.

Migrating SQL Server Database to Azure SQL Database

We will guide in an easy way with clearly defined steps to be followed in migrating On-premise SQL Server database to Azure SQL Database. Before going into details, it is must to have a good understanding of some of the terms:

Objective

This document is intended to serve as a plan for migrating on-premises and/or cloud IaaS Oracle databases and tables to Snowflake

Let us accept this. One is cost and second is continuous maintenance which in turns add to cost. Oracle is a relational database (RDBMS) that often comes with high licensing fees to host an enterprise’s most important data. Oracle also requires a significant amount of work to set up and maintain, both for hardware (network, storage, OS patching, configuration) and the software (configuration, updates). If there is a need to utilize the data on these systems for analytical purposes, there will often be additional licensing fees for the OLAP counterparts or the added risk of analytical workloads interfering with OLTP systems that are serving end users. All of these things can quickly add up to create a costly product that is taxing to both the budget and IT workforce.

The Situation

A multinational provider of Digital, IT Services and Solutions with over 20,000 employees, headquartered in California, USA, were accessing their data from an on-premise data warehouse to generate Business Intelligence (BI) reports.

Considering the advantages of a cloud-based data warehouse, the company wanted to move their on-premise data warehouse to a modern cloud-based data warehouse. Data Semantics advised the company to choose Microsoft Azure Data Warehouse for its benefits and ease-of-migration.

The Problem

Although migrating to Azure data warehouse cloud is always a better option, Cloud platforms have a new set of limitations and challenges, compared to the existing SQL Server on-premise data warehouse of the company.

Challenges in migrating to Cloud (Microsoft Azure Data Warehouse):