Data is currently the greatest driving force in business and social development globally. But how and where is your data being stored? Is it being stored safely? What tools are you utilizing to analyze this data? How much time do you spend managing and maintaining the underlying infrastructure housing your data?
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When storing data from different sources and of different structures and types, you will benefit from using a data lake. This will allow you to catalog your data ensuring the consistency, accuracy and reliability of your data across different data stores.
AWS Glue is able to catalog, process, and combine your data. It is a serverless data integration service, and provides seamless data integration. AWS Glue Jobs enables you to streamline your ETL (Extract, Transform, Load) workloads.
Data Transformation | ETL
AWS Glue Studio allows you to create scalable ETL jobs for distributed processing with ease. AWS Glue will automatically generate the code to extract, transform, and load your data, after you have define your ETL process, which can be done in a simple drag-and-drop job editor. The code is then generated in Scala or Python and written for Apache Spark.
AWS Glue jobs can be triggered on-demand, on a schedule, or based on an event (CloudWatch event or Lambda trigger). AWS Glue will handle everything including the inter-job dependencies, as well as filtering bad data, and retrying jobs if they fail. Logs and notifications are pushed to Amazon CloudWatch so you can monitor and get alerts for better insights and analytics.
With AWS Glue Crawler you can detect and discover metadata and data schemas across multiple data sources.
AWS Glue will also cater for any standardization and normalization needed for processing your data
AWS Lambda can use custom ETL scripts for workloads that require more flexibility in the code build out and that have custom package dependencies.
Migrating legacy data warehouses
Managed AWS analytics services will enable you to spend less time maintaining and managing the underlying infrastructure housing your data. This will give you more time to focus on how to use your data more effectively.
AWS has many database and analytics services, which will give you the ability to build complex data management environments. Additionally, these core services can reduce your operational overhead compared to legacy services.
Scalability is key in any database workload. In today’s technological market, databases constantly experience an increasing number of inserts, updates, and deletes, which can become intensive on the database’s input/output system. This is made worse with self-managed legacy database infrastructures that require hardware provisioning, maintenance, backups and patching to simply keep up with this demand. Amazon RDS is highly scalable and because of it’s on-demand nature it will free up time for database administrators while doing time-consuming tasks.
Amazon lake Formation
Transactional databases can experience storage issues and can lack optimization when running compute-intensive analytical queries. To alleviate these stresses, you can move your transactional database to a data lake. This also will provide you with a centralized repository with the ability of combining mixed data structures from different data sources for analytics and storage. AWS Lake Formation is built on Amazon Simple Storage Service (Amazon S3) and enables you to set up a data lake in a matter of days.
AWS Lake Formation allows you to integrate your centralized storage with most database engines supported by RDS by using the blueprint feature in AWS Lake Formation. By using AWS Lake Formation to create a data lake, you have the flexibility to keep your data lake updated with full copies of the database through database snapshots or through incremental data loads.
Data warehouses improve the performance of compute intensive analytics requirements across petabytes of data. Amazon Redshift is a fully managed data warehouse service that efficiently provides high performance for analytics queries.
Amazon Redshift reduces the operational overhead expected with legacy data warehouses by automating frequent tasks such as patching, backups, and hardware provisioning. Amazon Redshift cluster is where you can configure and customize the infrastructure and performance baselines for your data warehouse. Amazon Redshift also provides Redshift Spectrum, which allows you to use Amazon Redshift cluster resources to query and join data with Amazon S3.
Amazon Athena is a serverless query service that uses standard SQL to query data in Amazon S3. Because Amazon Athena is serverless there is no infrastructure to manage or underlying hardware to provision. You can run high-performance queries while benefiting from storage cost savings associated with the data lake built on Amazon S3. Amazon Athena is serverless, meaning you only pay for the amount of data that is scanned with each query.
Amazon QuickSight is a powerful Business Intelligence (BI) dashboard service that allows your organization to gain in-depth insights and advanced analytics. QuickSight dashboards are customisable and interactive, allowing users to use natural language and conversational questions to receive relevant information and visualizations. These natural language capabilities are powered by machine learning, thanks to AWS’s AI/ML advancements. QuickSight’s serverless dashboards can connect to petabytes of data in Amazon S3 and allow for data querying using Amazon Athena.
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