Today businesses all around the world trying to leverage their data to quickly make better decisions as changes take place. To achieve this agility, they must connect previously compartmentalized terabytes to petabytes, and occasionally exabytes, of data to have a comprehensive understanding of their clients and business processes. This strategy cannot be handled by conventional on-premises data analytics tools due to their scalability issues and high cost. As a result, the number of companies wishing to upgrade their data and analytics infrastructure by switching to the cloud is increasing.
Customer data in the real world
Many businesses are consolidating all of their data from several layers into a single location, frequently referred to as a "data lake," to do analytics and machine learning (ML) on these enormous amounts of data. The performance, scale, and cost advantages that purpose-built data storage offer for particular use cases is another reason why these same businesses store data there. These data stores include data warehouses, which can swiftly respond to complicated queries on structured data, and tools like Elastic search and OpenSearch, which can search and analyze log data quickly and can be used to check on the health of production systems. A data analytics approach that is one size fits all is no longer effective since it always results in compromises.
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Customers must be able to move data across these systems with ease if they are to benefit fully from their data lakes and these purpose-built repositories. For instance, clickstream information from web applications can be instantly gathered in a data lake and then some of that information can be transferred to a data warehouse for daily reporting. This idea is what we refer to as inside-out data movement.
Customers similarly move data the reverse way, from the outside in. For instance, they copy query results for product sales in a certain region from their data warehouse into their data lake to apply machine learning to a bigger data set and run product recommendation algorithms.
Hence, in some circumstances, consumers desire to migrate data around the perimeter, or from one purpose-built data repository to another. For instance, to make it simpler to browse their product catalog and offload the search queries from the database, they can replicate the product catalog data contained in their database to their search engine.
Moving all of this data around is more difficult as the amount of data in these data lakes and purpose-built repositories increases. This is known as data gravity.
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Customers must be able to use a central data lake and a ring of specifically designed data services around that data lake to make decisions quickly and effectively. They must also recognize the importance of data by making it simple for users to move the information they require across different data silos in a controlled and secure manner.
Customers need a data architecture that can support the following to satisfy these needs:
• Quickly creating a scalable data lake.
• Making advantage of a large and diverse range of data services that have been specifically designed to offer the performance needed for use cases like interactive dashboards and log analytics.
• Flow-through data movement within and across purpose-built data services, as well as between those services.
• Ensuring compliance by using a standardized approach to safeguard, oversee, and regulate data access.
• Low-cost system scaling without performance degradation.
This cutting-edge method of analytics is known as the Lake House Architecture.
Lake House Architecture on AWS
The concept that using a one-size-fits-all strategy for analytics eventually results in compromises is acknowledged by A Lake House Architecture. To enable uniform governance and simple data migration, it is necessary to integrate a data lake, a data warehouse, and purpose-built stores in addition to just integrating them. On AWS, the Lake House Architecture is depicted in the diagram below.
Let's examine how the Lake House Architecture works with AWS.
Scalable data lakes
Because it has unmatched durability, availability, and scalability, as well as the best security, compliance, and audit capabilities, the fastest performance at the lowest cost, the most ways to bring in data, and the most partner integrations, Amazon Simple Storage Service (Amazon S3) is the best platform on which to build a data lake.
It takes a lot of human work and effort to set up partitions, activate encryption, manage keys, reorganize data into the columnar format, grant access, and audit access, just to name a few of the laborious manual chores involved in managing and setting up data lakes. We created AWS Lake Formation to make this process simpler. Our customers can create secure data lakes in the cloud with Lake Formation in days as opposed to months. Data is gathered and cataloged from databases and object storage, moved into an Amazon S3 data lake, cleaned up and categorized using machine learning (ML) methods, and secured access to sensitive data is provided via Lake Formation.
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Additionally, amazon unveiled three new features for AWS Lake Formation in preview: query acceleration through automatic file compaction, ACID transactions, and regulated tables for concurrent modifications and consistent query results. A new data lake table type called a governed table is used in the preview to expose new APIs that support atomic, consistent, isolated, and durable (ACID) transactions. While still enabling other users to execute analytical queries and ML models on the same data sets concurrently, governed tables allow multiple users to insert, delete, and edit rows across tables. Small files are automatically combined into larger files to speed up queries by up to seven times.
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Purpose-built analytics services
With Amazon Athena, Amazon EMR, Amazon OpenSearch Service, Amazon Kinesis, and Amazon Redshift among its array of services, AWS has the broadest and deepest selection of services designed specifically for analytics. Since each of these services is designed to be the best in its class, using them never requires you to give up performance, scale, or affordability. For instance, Apache Spark on EMR runs 1.7 times faster than standard Apache Spark 3.0, and Amazon Redshift offers up to three times better price performance than other cloud data warehouses. As a result, petabyte-scale analysis can be performed for less than half the price of conventional on-premises solutions.
Amazon is constantly coming up with new features and capabilities in these services that are specifically designed to fulfill the needs of our customers. For instance, they introduced the general availability of Amazon EMR on Amazon Elastic Kubernetes Service (EKS) to assist with additional cost savings and deployment flexibility. This provides a new fully managed Amazon EMR deployment option on Amazon EKS. Customers had to decide whether to run managed Amazon EMR on EC2 or self-manage their own Apache Spark on Amazon EKS till now. Now that analytical workloads may coexist on the same Amazon EKS cluster as microservices and other Kubernetes-based applications, better resource utilization, easier infrastructure administration, and the usage of a single set of monitoring tools are all made possible.
We have made Automatic Table Optimisations (ATO) for Amazon Redshift generally available to improve data warehousing performance. By automating optimization activities like establishing distribution and sort keys to give you optimal performance without the expense of manual performance tuning, ATO makes it easier to tune the performance of Amazon Redshift data warehouses.
To make it even simpler and quicker for your business users to extract insights from your data, Amazon also announced the preview of Amazon QuickSight Q. With the aid of machine learning, QuickSight Q creates a data model that recognizes the links and significance of business data. It gives users the ability to quickly and accurately ask ad hoc queries about their business data in human-readable language. As a result, business customers no longer have to wait for modeling by understaffed business intelligence (BI) teams to receive answers to their queries.
Seamless data movement
Customers must be able to transfer data effortlessly between all of their services and data stores: inside-out, outside-in, and around the perimeter since data is kept in a variety of different systems. No other analytics supplier makes it as simple to transfer data at scale to critical locations. Data preparation for analytics, machine learning, and application development is made simple with the help of AWS Glue, a serverless data integration tool. AWS Glue offers all the tools required for data integration, allowing insights to be obtained quickly rather than over several months.
Federated queries, which can be executed on data stored in operational databases, data warehouses, and data lakes to provide insights across multiple data sources without requiring data movement or the setup and upkeep of complex extract, transform, and load (ETL) pipelines, are supported by both Amazon Redshift and Athena.
Without the need to make copies or deal with the difficulty of transferring data around, data sharing offers a safe and simple way to communicate live data across numerous Amazon Redshift clusters both inside and outside the organization. To fulfill the performance needs of each task and track consumption by each business group, customers can utilize data sharing to run analytics workloads that use the same data in different compute clusters. To enable workload isolation and chargeback, for instance, companies can build up a central ETL cluster and share data with various BI clusters.
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