Leverage Modern Analytics with AWS Data Warehousing Service

A data warehouse is a centralized repository for data entering from multiple data sources. Generally, data comes from transactional systems and other relational databases to the warehouse, including every type of structured, semi-structured and unstructured data. Then the data processing, transformation and ingestion are done at regular intervals. Data users can access the data through various BI tools, SQL clients and spreadsheets. AWS Data Warehousing Service for enterprises takes away all the worries of storing big data and executes modern analytics to deliver actionable insights.

Modern Analytics and Architecture

The architecture of Modern Analytics pipelines is designed to manage large volumes of flowing data streams from a variety of sources like databases, applications and devices.

A modern analytics pipeline generally consists of the following phases:

  1. 1. Data Collection
  2. 2. Data Storage
  3. 3. Data Processing
  4. 4. Data Analyzing and Visualizing

Data Collection

At this stage of data collection, consider there are different types of data coming from various sources. The types of data available are transactional data, log data, streaming data and Internet of Things (IoT) data. Amazon Web Services (AWS) have solutions to store every type of data storage in a warehouse.

Data Processing

The data was collected in the previous stage that possibly contains useful information. You can use that data for analysis and extract important information for business intelligence that will help you in business growth. Business intelligence delivers important information, such as the behavior of your users and customers and the popularity of your products in the market. The best approach to extract this intelligence is performing analytics after loading your raw data into a data warehouse.

There are two types of data processing in workflows: batch and real-time. OLAP (online analytic processing) and OLTP are the two most common forms of processing. Generally, OLAP processing is batch-based, while OLTP systems are based on real-time processing.

Data Storage

Either you can store your data in a data warehouse or a data mart. Each one has its varied benefits and usages. You can use a data warehouse as a central repository system for diverse types of data to run quick analytics on massive data amounts, while the use of data mart is restricted to a particular application only for a specific region of data.

Analysis and Visualization

After data processing and preparing it for further analysis, you require the correct tools to analyze and visualize the processed data. In several cases, you can execute data analysis with the same tools you use for data processing. You can make use of tools like SQL Workbench for data analysis in Amazon Redshift with ANSI SQL. Amazon Redshift also works well by integrating it with other famous third-party BI solutions available in the market.

 

 

Technology Options of Data Warehouse

There are three technologies options made available for building an enterprise data warehouse on AWS successfully. The options are:

  • Row-Oriented Databases
  • Column-Oriented Databases
  • Massively Parallel Processing Architectures

Leverage the power of Amazon Redshift!

Amazon Redshift provides fast query and input/output performance facility for all data sizes via columnar storage and sending queries across numerous nodes. It helps in automating almost all normal administrative tasks such as provisioning, configuring, monitoring, backing up and securing of data warehouse easily in an inexpensive manner. With AWS Data Warehousing Service, you can create petabyte-scale data warehouses in a few minutes only as compared to other lengthy traditional on-premises deployments.  Contact ExistBI’s experienced consultants offering services in the United States, United Kingdom and Europe.

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