Design and Implement a Analytical Platform Using Python
Analytics on AWS
Fastest way to get answers from all your data to all your users
AWS provides the broadest selection of analytics services that fit all your data analytics needs and enables organizations of all sizes and industries to reinvent their business with data. From data movement, data storage, data lakes, big data analytics, and machine learning (ML) to anything in between, AWS offers purpose-built services that provide the best price performance, scalability, and lowest cost.
Store data at any scale
AWS analytics services are built to handle large amounts of data at scale and automate many manual and time-consuming tasks. AWS-powered data lakes, supported by the unmatched availability of Amazon Simple Storage Service (S3), can handle the scale, agility, and flexibility required to combine different data and analytics approaches. Use AWS analytics services to gain deeper insights than with traditional data silos and data warehouses.
Purpose-built for performance and cost
AWS is the fastest and most cost-effective place to store and analyze data. AWS analytics tools are purpose-built to help you quickly extract data insight using the most appropriate tool for the job, and optimized to give you the best performance, scale, and cost for your needs.
Unified data access, security, and governance
AWS provides a comprehensive set of tools that go beyond standard security functionality, like encryption and access control, to offer unified security policy management and proactive monitoring. Centrally define and manage your security, governance, and auditing policies to satisfy industry- and geography-specific regulations.
Machine learning integration
AWS offers built-in ML integration as part of our purpose-built analytics services. You can build, train, and deploy ML models quickly with Amazon SageMaker—a fully managed service that provides tools for every step of the ML development lifecycle in one integrated environment.
AWS Analytics - Modern Data Strategy (2:15)
10,000+
data lakes run on AWS
3X
faster with Amazon EMR than standard Apache Spark
50%
less expensive than other cloud data warehouses
70%
savings on storage cost for data in data lakes
3 PB
of data storage in a single cluster with Amazon OpenSearch Service (successor to Amazon Elasticsearch Service)
Use cases
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Analytics & data warehousing
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Predictive analytics & ML
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Analytics & data warehousing
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Analytics & data warehousing
AWS provides the broadest and most cost-effective set of analytics services to help you gain insights faster from all your data.
Broadest selection of analytics services
Each analytics service is purpose-built for a wide range of analytics use cases such as interactive analysis, big data processing, data warehousing, real-time analytics, operational analytics, dashboards, and visualizations.
Services
Beyond all of the certifications and best practices you would expect from AWS, we also have security features designed to help you stay compliant with your best practices and industry regulations.
Price-performant
AWS is committed to providing the best performance at the lowest cost across all analytics services, and we are continually innovating to improve the price performance of our services.
Resources
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Data movement
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Data movement
AWS makes it easy for you to combine, move, and replicate data across multiple data stores and your data lake.
Ease of use
AWS allows you to easily move data between the data lake and purpose-built data services. For example, AWS Glue is a serverless data integration service that makes it easy to prepare data for analytics, machine learning, and application development.
Faster data integration
AWS gives you the ability to query data across different data sources such as databases, data lakes, and data warehouses. For example, Amazon Athena enables you to use SQL to query a data lake and federated query lets you query live data from relational databases.
Ease of movement
With data stored in a number of different systems, AWS allows you to easily move that data between all of your services and data stores: inside out, outside in, and around the perimeter.
Resources
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Data lake
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Data lake
Tens of thousands of customers run their data lakes on AWS.
Scalable
Collect, store, organize, and analyze data from multiple sources and formats and scale it to any size. Use AWS Lake Formation to automate tasks required to set up a data lake while saving time defining data structures, schema, and transformations.
Flexible
Easily ingest data in a variety of ways, including leveraging Amazon Kinesis, AWS Import/Export Snowball, AWS Direct Connect, and more. Store all of your data, regardless of volume or format, using Amazon Simple Storage Service (Amazon S3).
Agile
Deploy the infrastructure you need almost instantly. This means teams can be more productive, easily try new things, and roll out projects sooner.
Resources
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Predictive analytics & ML
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Predictive analytics & ML
For predictive analytics use cases, AWS provides a broad set of machine learning services and tools that run on your data lake on AWS.
Deeper and faster insights
AWS analytics services leverage proven machine learning (ML) and natural language capabilities to help you gain deeper and faster insights from your data.
Platform integration
AWS provides built-in ML integration as part of its purpose-built data stores and analytics services, allowing you to create, train, and deploy ML models using familiar languages like SQL.
Experience
AWS is committed to providing the best performance at the lowest cost across all analytics services and we are continually innovating to improve the price-performance of our services.
Resources
Customers
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Moderna
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Moderna runs all its SAP S/4HANA workloads on AWS, including manufacturing, accounting, and inventory management, which enables the company to achieve greater efficiency and visibility across its operations. Moderna uses Amazon Redshift as a central repository for all the data it captures and stores backups in Amazon S3.
Read the case study
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Invista
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INVISTA migrated from siloed data to a data lake on AWS. The company built a modern data architecture with AWS analytics services to transform their manufacturing workstream, use data to remove manual processes, and unlock the potential of its digital plant. INVISTA saved more than $2 million per year and has created $300 million in value from company-wide data.
Read the case study
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Intuit
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Intuit migrated to an Amazon Redshift-based solution that scales to more than 7X the data volume with zero effort and delivers 20X performance over the company's previous solution. This resulted in a 90 percent reduction in time-to-insight, and a 66 percent cost reduction.
Watch the video
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Pinterest
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Pinterest scaled daily log search and analytics to 1.7 TB and reduced cost by 30 percent by moving to managed analytics using Amazon OpenSearch Service (successor to Amazon Elasticsearch Service). The company scaled its log analysis capabilities to reduce operational burdens, improve security, and reduce costs.
Read the case study
Get started
AWS Data-Driven Everything
In the AWS Data-Driven EVERYTHING (D2E) program, AWS will partner with our customers to move faster, with greater precision and a far more ambitious scope to jump-start your own data flywheel.
Learn more »
AWS Data Lab
AWS Data Lab offers accelerated, joint engineering engagements between customers and AWS technical resources to create tangible deliverables that accelerate data and analytics modernization initiatives.
Learn more »
AWS analytics and big data reference architecture
Learn architecture best practices for cloud data analysis, data warehousing, and data management on AWS.
Learn more »
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Design and Implement a Analytical Platform Using Python
Source: https://aws.amazon.com/big-data/datalakes-and-analytics/
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