Cloud

Databricks

A massive amount of data is being produced (estimated 2.5+ quintillion bytes/daily) by different sources like social media platforms, websites, IoT devices, corporate databases and many more. Millions of users are connected 24/7/365 sharing information, uploading images and videos in social media platforms or any other websites/databases. So the question arises how this huge amount of data can be managed and leveraged for business decisions. That’s when Databricks comes into picture.

Databricks is an unified data analytics platform for data engineering, data science, machine learning and analytics. It allows business analysts, data engineers to build models & ETL pipelines, and deploy business process workflows using their platform. Apache Spark is the core of Databricks which is widely used in the industry for developing big data projects. Databricks is available as a service in Microsoft Azure, Amazon Web Services and Google Cloud Platform.

 

Big Data

In the modern world, data is much larger and complex. […]

Know about Snowflake – Ur Data. No limits.

Introduction

Snowflake is a data warehouse built for the cloud which delivers a capable solution in resolving issues for which legacy, cloud data platforms and on-premises data warehouse were not designed. Snowflake works with leading data management, data integration and BI partners to bring together all data and enable the users to perform cutting-edge analytics.

Snowflake is the first analytical database that leverages the power of cloud. Adapting snowflake is simple and it offers great performance and concurrency. It supports distributed architecture, data protection, query resiliency and significantly maintain fault tolerance. In addition, snowflake services can be run on a public cloud infrastructure.

Snowflake architecture is divided into three layers, they are:

  1. Cloud services
  2. Virtual warehouses
  3. Database storage

Functionality of Cloud Data Warehouse

Data warehouse is basically a relation database which is exclusively designed for query and analysis as a substitute of transaction process. But it holds resulting historical data from a transaction data.

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