In the modern business environment, data analytics is the backbone of many businesses.
Today, it is the enabler of the managerial decision-making process; every decision you make, ranging from what to produce to how distribution channels should be set up, is based on insights produced by data analytics solutions.
Your organisation, therefore, needs to invest in robust systems that collect, store, and analyse large amounts of data efficiently, helping you gain a significant competitive advantage over your competitors.
While most organisations still rely on an on-site data analytics implementation, future-focused businesses have migrated to cloud-based computing environments like Amazon Web Services (AWS) to implement cost-effective and scalable data analytics solutions.
AWS allows you to configure your cloud infrastructure to suit your specific analytics requirements. It helps you optimise the data analytics pipeline, which consists of data collection, storage, processing and visualisation.
In this post, we dive deeper into how AWS configuration can help you deploy better cloud-based analytics platforms by optimising the analytics pipeline.
Data collection is the most important step in your data analytics pipeline, as it delivers the resources the analytics platform needs to produce actionable insights.
To create the best data collections system for your business needs, you need to consider ingestion frequency, latency, cost and the durability of data collection processes.
The first thing you need to consider when configuring your AWS environment is how often data is sent through your data collection system. There are three distinct ingestion frequencies: hot, warm and cold.
The ingestion frequency determines what kind of AWS configuration you will need to meet your data ingestion requirements.
Transactional data, for example, does not require your AWS environment to be configured to facilitate constant data ingestion. This type of data is better ingested using the Amazon Data Migration service.
Real-time data, on the other hand, requires tools such as Kinesis Firehose and Kinesis Data Streams.
Your data storage requirements will be determined by your data collection systems. That said, every AWS data analytics deployment uses two different data storage methods: data warehouse and data lakes.
A data warehouse stores structured data; this means that the data stored in your data warehouse is cleaned, enriched and processed. Storing data like this is ideal for operational reporting and analysis; you can configure and build data warehouses in AWS using the Redshift tool.
Data lakes, meanwhile, store structured and unstructured data. This particular data storage method can store relational data from applications, and nonrelational data generated from other sources. Data lakes can be built using the S3 tool during the AWS configuration process.
Depending on your needs, you can configure your AWS environment to prioritise either data storage methods.
Raw, unstructured data is not useful in your decision-making process. To drive value in decisions, you need actionable and accurate insights from your cloud-analytics platforms.
To produce actionable insights, your data analytics platforms need to be fed relational data that is free of errors, duplicate entries and unrelated fields. The process of making sure unstructured data is free of errors and suitable for the analysis process is called data preparation.
Data preparation, however, is not a straightforward task. You need to extract data from various sources, clean and organise it into the required format, and then load it into data warehouses. In traditional analytics workflows, data scientists spend more than 75% of their time preparing data for analytics.
AWS offers you several automated tools for data preparation such as AMDA, Amazon EMR and AWS Glue. You need to configure your cloud environment with the tools that best suit your data preparation needs. These tools reduce the time spent on data preparation significantly.
The final step in the analytics pipeline is data visualisation. At this stage, data is pulled from data warehouses, curated and analysed, and presented as useful information to you.
To facilitate visualisation, you need to configure your AWS environment using the Amazon QuickSight tool during the configuration process. It is a robust, cloud-powered data visualisation tool that augments your data analytics capabilities.
As data analytics requirements continue to evolve, many organisations are beginning to migrate their analytics infrastructure to the cloud.
With AWS being the premier cloud service for analytics needs, understanding how to configure it can help you optimise your cloud-data analytics infrastructure.