Building and maintaining data management and analytics solutions is an increasingly important element in companies’ business strategy. Data analysis enables you to extract meaningful information and make strategic decisions to maintain a competitive edge in the market. AWS (Amazon Web Services) offers a suite of cloud services on demand that allows you to build data management and analytics architectures efficiently and scalable. In this article we will highlight the key services offered by AWS and the “best practices“, going through the data life cycle: data acquisition, storage, analysis and consumption.
The first step in a Data Management & Analytics process is the acquisition of data from various sources within the AWS infrastructure. This step is often complicated due to the heterogeneity of the sources and the data itself. AWS provides services such as Amazon Kinesis, AWS Gluee AWS Data Pipeline that simplify the ingestion process, allowing you to efficiently collect, process and transform data from heterogeneous sources.
AWS offers a variety of data storage services, depending on your needs. The main one is Amazon S3, an extremely scalable object storage service that offers great durability and data availability, also suitable for creating Data Lake. S3 automatically replicates and distributes data to data centers across different regions to ensure durability and avoid data loss.
For structured data, Amazon RDS offers a fully managed relational database service, while Amazon Redshift allows you to create Data Warehouses. There are also services for non-relational databases, such as Amazon DynamoDB and Amazon DocumentDB.
Data analysis is the set of data transformation processes whose complexity increases as the amount of data increases. AWS offers services capable of processing and analyzing data in a scalable way such as Amazon EMR, which allows you to run Big Data frameworks such as Apache Spark and Hadoop, and AWS Glue, which is the main ETL (Extract, Transform and Load) service for the AWS environment. Glue simplifies data transformation with a serverless approach and low-code interface.
AWS offers a variety of advanced data visualization and analytics services. Amazon Athena allows you to launch SQL-style queries on serverless structured file sets, while Amazon Quicksight is a fully managed Business Intelligence service that allows you to create interactive dashboards and reports, integrated also with functions based on Artificial Intelligence. Amazon SageMaker is the leading AWS service for developing and customizing advanced analytics solutions based on Artificial Intelligence (AI) and Machine Learning (ML).
Data Governance & Security
Alongside the life path of data, it is important to keep in mind the issues of Data Governance & Security. Managing and monitoring data flows is critical to the Data Management architecture, improving scalability and flexibility. AWS IAM (Identity and Access Management) allows you to easily manage policies and permissions for users and processes, while services such as Amazon CloudWatch and Amazon CloudTrail allow you to monitor processes and logs, as well as review the operations carried out. Orchestrating processes becomes easier thanks to AWS Step Functions and AWS Data Pipeline, while Amazon SageMaker allows you to create and monitor end-to-end systems based on machine learning capabilities. Finally, to ensure data security, AWS provides various services to encrypt data (in transit and at rest) such as AWS KMS (Key Management Service) and AWS Secret Manager, as well as the ability to create private virtual networks via Amazon VPC.
AWS solutions are natively scalable and mutually integrated to simplify the construction of secure and reliable pipelines, allowing companies to improve the data-driven organizational approach and extract meaningful information from data. Learn how Blue BI can help you build a complete Data Management & Analytics architecture using AWS services.