Analytics Flashcards Preview

AWS 2018 - Products > Analytics > Flashcards

Flashcards in Analytics Deck (10):
1

Amazon Athena



Analytics

Amazon Athena | Analytics


Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to setup or manage, and you can start analyzing data immediately. You don’t even need to load your data into Athena, it works directly with data stored in S3. To get started, just log into the Athena Management Console, define your schema, and start querying. Amazon Athena uses Presto with full standard SQL support and works with a variety of standard data formats, including CSV, JSON, ORC, Apache Parquet and Avro. While Amazon Athena is ideal for quick, ad-hoc querying and integrates with Amazon QuickSight for easy visualization, it can also handle complex analysis, including large joins, window functions, and arrays.

2

Amazon CloudSearch



Analytics

Amazon CloudSearch | Analytics


Amazon CloudSearch is a fully-managed service in the AWS Cloud that makes it easy to set up, manage, and scale a search solution for your website or application.

3

Amazon Elasticsearch Service



Analytics

Amazon Elasticsearch Service | Analytics


Amazon Elasticsearch Service is a managed service that makes it easy to deploy, operate, and scale Elasticsearch clusters in the AWS Cloud.

4

Amazon EMR



Analytics

Amazon EMR | Analytics


Amazon EMR is a web service that enables businesses, researchers, data analysts, and developers to easily and cost-effectively process vast amounts of data. It utilizes a hosted Hadoop framework running on the web-scale infrastructure of Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Simple Storage Service (Amazon S3).

5

Amazon Kinesis Data Analytics



Analytics

Amazon Kinesis Data Analytics | Analytics


Amazon Kinesis Data Analytics is the easiest way to process and analyze real-time, streaming data. With Amazon Kinesis Data Analytics, you just use standard SQL to process your data streams, so you don’t have to learn any new programming languages. Simply point Kinesis Data Analytics at an incoming data stream, write your SQL queries, and specify where you want to load the results. Kinesis Data Analytics takes care of running your SQL queries continuously on data while it’s in transit and sending the results to the destinations.

6

Amazon Kinesis Data Firehose



Analytics

Amazon Kinesis Data Firehose | Analytics


Amazon Kinesis Data Firehose is the easiest way to load streaming data into data stores and analytics tools. It can capture, transform, and load streaming data into Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk, enabling near real-time analytics with existing business intelligence tools and dashboards you’re already using today. It is a fully managed service that automatically scales to match the throughput of your data and requires no ongoing administration. It can also batch, compress, and encrypt the data before loading it, minimizing the amount of storage used at the destination and increasing security.

7

Amazon Kinesis Video Streams



Analytics

Amazon Kinesis Video Streams | Analytics


Amazon Kinesis Video Streams makes it easy to securely stream video from connected devices to AWS for analytics, machine learning (ML), and other processing. Kinesis Video Streams automatically provisions and elastically scales all the infrastructure needed to ingest streaming video data from millions of devices. It also durably stores, encrypts, and indexes video data in your streams, and allows you to access your data through easy-to-use APIs. Kinesis Video Streams enables you to quickly build computer vision and ML applications through integration with Amazon Rekognition Video and libraries for ML frameworks such as Apache MxNet, TensorFlow, and OpenCV.

8

Amazon Redshift



Analytics

Amazon Redshift | Analytics


Amazon Redshift is a fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. It allows you to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution. Most results come back in seconds. With Redshift, you can start small for just $0.25 per hour with no commitments and scale out to petabytes of data for $1,000 per terabyte per year, less than a tenth the cost of traditional solutions. Amazon Redshift also includes Amazon Redshift Spectrum, allowing you to directly run SQL queries against exabytes of unstructured data in Amazon S3. No loading or transformation is required, and you can use open data formats, including Avro, CSV, Grok, ORC, Parquet, RCFile, RegexSerDe, SequenceFile, TextFile, and TSV. Redshift Spectrum automatically scales query compute capacity based on the data being retrieved, so queries against Amazon S3 run fast, regardless of data set size.

Traditional data warehouses require significant time and resource to administer, especially for large datasets. In addition, the financial cost associated with building, maintaining, and growing self-managed, on-premise data warehouses is very high. As your data grows, you have to constantly trade-off what data to load into your data warehouse and what data to archive in storage so you can manage costs, keep ETL complexity low, and deliver good performance. Amazon Redshift not only significantly lowers the cost and operational overhead of a data warehouse, but with Redshift Spectrum, also makes it easy to analyze large amounts of data in its native format without requiring you to load the data.

Amazon Redshift gives you fast querying capabilities over structured data using familiar SQL-based clients and business intelligence (BI) tools using standard ODBC and JDBC connections. Queries are distributed and parallelized across multiple physical resources. You can easily scale an Amazon Redshift data warehouse up or down with a few clicks in the AWS Management Console or with a single API call. Amazon Redshift automatically patches and backs up your data warehouse, storing the backups for a user-defined retention period. Amazon Redshift uses replication and continuous backups to enhance availability and improve data durability and can automatically recover from component and node failures. In addition, Amazon Redshift supports Amazon Virtual Private Cloud (Amazon VPC), SSL, AES-256 encryption and Hardware Security Modules (HSMs) to protect your data in transit and at rest.

As with all Amazon Web Services, there are no up-front investments required, and you pay only for the resources you use. Amazon Redshift lets you pay as you go. You can even try Amazon Redshift for free.

9

AWS Data Pipeline



Analytics

AWS Data Pipeline | Analytics


AWS Data Pipeline is a web service that makes it easy to schedule regular data movement and data processing activities in the AWS cloud. AWS Data Pipeline integrates with on-premise and cloud-based storage systems to allow developers to use their data when they need it, where they want it, and in the required format. AWS Data Pipeline allows you to quickly define a dependent chain of data sources, destinations, and predefined or custom data processing activities called a pipeline. Based on a schedule you define, your pipeline regularly performs processing activities such as distributed data copy, SQL transforms, MapReduce applications, or custom scripts against destinations such as Amazon S3, Amazon RDS, or Amazon DynamoDB. By executing the scheduling, retry, and failure logic for these workflows as a highly scalable and fully managed service, Data Pipeline ensures that your pipelines are robust and highly available.

10

AWS Glue



Analytics

AWS Glue | Analytics


AWS Glue is a fully-managed, pay-as-you-go, extract, transform, and load (ETL) service that automates the time-consuming steps of data preparation for analytics. AWS Glue automatically discovers and profiles your data via the Glue Data Catalog, recommends and generates ETL code to transform your source data into target schemas, and runs the ETL jobs on a fully managed, scale-out Apache Spark environment to load your data into its destination. It also allows you to setup, orchestrate, and monitor complex data flows.