React Fast by Processing Streaming Data in Real-Time
•Download as PPTX, PDF•
1 like•1,361 views
Real-time processing of streaming data is a common architectural pattern used in many applications. Amazon Kinesis Analytics is the easiest way to process streaming data in real time with standard SQL without having to learn new programming languages or processing frameworks. We will present how to use Amazon Kinesis Analytics on streaming data and gain actionable insights from your data. Services: Amazon Kinesis Analytics, Amazon Redshift, Amazon Elastic Search Service and Amazon S3. Presenters: Kobi Biton & Ran Tessler
Recommended
More Related Content
What's hot
What's hot (20)
Viewers also liked
Viewers also liked (20)
Similar to React Fast by Processing Streaming Data in Real-Time
Similar to React Fast by Processing Streaming Data in Real-Time (20)
More from Amazon Web Services
More from Amazon Web Services (20)
Recently uploaded
Recently uploaded (20)
React Fast by Processing Streaming Data in Real-Time
- 1. React Fast by Processing Streaming Data Kobi Biton, Solutions Architect, AWS Ran Tessler, Mgr. Solutions Architecture, AWS
- 2. SpoTaxi
- 3. Mobile Apps Web Clickstream Application Logs Metering Records IoT Sensors Smart Buildings [Wed Oct 11 14:32:52 2000] [error] [client 127.0.0.1] client denied by server configuration: /export/home/live/ap/ht docs/test Most data is produced continuously
- 4. Recent data is highly valuable • If you act on it in time • Perishable Insights (M. Gualtieri, Forrester) Old + Recent data is more valuable • If you have the means to combine them The diminishing value of data
- 5. • Durable • Continuous • Fast • Correct • Reactive • Reliable What are the key requirements? Ingest Transform Analyze React Persist Processing real-time, streaming data
- 6. Amazon Kinesis Streams Easy administration: Create a stream, set capacity level with shards. Scale to match your data throughput rate & volume. Build real-time applications: Process streaming data w/ Kinesis Client Library (KCL), Apache Spark/Storm, AWS Lambda,... Low cost: Cost-efficient for workloads of any scale.
- 7. Amazon Kinesis Firehose Zero administration: Capture and deliver streaming data to Amazon S3, Redshift, Elasticsearch w/o writing an app or managing infrastructure. Direct-to-data store integration: Batch, compress, and encrypt streaming data for delivery in as little as 60 seconds Seamless elasticity: Seamlessly scales to match data throughput w/o intervention Capture and submit streaming data to Firehose Analyze streaming data using your favorite BI tools Firehose loads streaming data continuously into S3, Amazon Redshift and Amazon Elasticsearch
- 8. Amazon Kinesis Analytics Apply SQL on streams: Easily connect to a Kinesis Stream or Firehose Delivery Stream and apply SQL skills. Build real-time applications: Perform continual processing on streaming big data with sub-second processing latencies. Easy Scalability : Elastically scales to match data throughput. Connect to Kinesis streams, Firehose delivery streams Run standard SQL queries against data streams Kinesis Analytics can send processed data to analytics tools so you can create alerts and respond in real-time
- 9. Use SQL to build real-time applications Easily write SQL code to process streaming data Connect to streaming source Continuously deliver SQL results
- 10. A streaming table is a STREAM • In relational databases, you work with SQL tables • With Kinesis Analytics, you work with STREAMs • SELECT, INSERT, and CREATE can be used with STREAMs CREATE STREAM Tweets (author VARCHAR(20), text VARCHAR(140)); INSERT INTO Tweets SELECT …
- 11. A simple streaming query • Tweets about the DLD Festival Summit • Selecting from a STREAM of tweets, an in-application stream • Each row has a corresponding ROWTIME SELECT STREAM ROWTIME, author, text FROM Tweets WHERE text LIKE ‘%#DLDTelAviv%'
- 12. Writing queries on unbounded datasets • Streams are unbounded data sets • Need continuous queries, row by row or across rows • WINDOWS define a start and end to the query SELECT STREAM author, count(author) OVER ONE_MINUTE FROM Tweets WINDOW ONE_MINUTE AS (PARTITION BY author RANGE INTERVAL '1' MINUTE PRECEDING);
- 13. Different types of Windows Tumbling Sliding
- 14. Amazon Kinesis: Streaming Data Made Easy Services make it easy to capture, deliver and process streams on AWS Amazon Kinesis Firehose For all developers, data scientists Easily load massive volumes of streaming data into Amazon S3, Redshift ElasticSearch Amazon Kinesis Streams For Technical Developers Collect and stream data for ordered, replayable, real-time processing Amazon Kinesis Analytics For all developers, data scientists Easily analyze data streams using standard SQL queries
- 15. Demo – detailed architecture taxi-telemetry Kinesis Stream taxi-stats Kinesis Stream CalculateStats KinesisAnalytics PipeStatsToDDB AWS Lambda stats Amazon DynsmoDB taxi-telemetry-to-s3 Kinesis Firehose spottaxi-data Amazon S3 PipeTelemetryToFirehose AWS Lambda spottaxi Amazon Elasticsearch Service PipeTelemetryToES AWS Lambda
Editor's Notes
- Narrative: The reality is that most data is produced continuously and is coming at us at lightning speeds due to an explosive growth of real-time data sources. TP: Machine data will make up 40% of our digital universe by 2020 TP: Over 60MM units will be deployed by retailers and bank branches by 2019 Narrative: Whether it is log data coming from mobile and web applications, purchase data from ecommerce sites, or sensor data from IoT devices, it all delivers information that can help companies learn about what their customers, organization, and business are doing right now. TP: Customer Benefits Improve operational efficiencies, improve customer experiences, new business models Smart building: reduce energy costs, cut maintenance, increase safety and security Smart textiles: monitor skin temperature, monitor stress
- Narrative: So how much is this data worth? Well, it depends… Recent data is highly valuable If you act on it in time Perishable Insights (M. Gualtieri, Forrester) Old + Recent data is more valuable If you have the means to combine them Narrative: Processing real-time data as it arrives can let you make decisions much faster and get the most value from your data. But, building your own custom applications to process streaming data is complicated and resource intensive. You need to train or hire developers with the right skillsets, and then wait for months for the applications to be built and fine-tuned, and the operate and scale the application as the business grows. All of this takes lots of time and money, and, at the end of the day, lots of companies just never get there, settle for the status-quo, and live with information that is hours or days old.
- Narrative: You need a different set of analytical tools to collect and analyze real-time streaming data than what you have traditionally used for data at rest. With traditional analytics, you gather the information, store it in a database, and analyze it hours, days, or weeks later. Analyzing real-time data requires a different approach. Instead of running database queries on stored data, streaming analytics platforms have to process the data continuously and before the data lands in a database. And streaming data comes in at an incredible rate that can vary up and down all the time. Streaming analytics platforms have to be able to process this data when it arrives, often at speeds of millions and even tens of millions of events per hour. Key requirements of stream processing Durable: Durable ingest so that processing can be repeatable; Continuous - Need to always be processing the latest data Fast: Frequency (micro batches, size of batches, true streaming), and speed (sub-second, minute, hour) Correct: at most once, at least once, and exactly once processing; event time, ingest time, processing time. Reactive: Ability to process and respond in near real-time; feedback mechanisms to send processed data to live applications Reliable: Highly available, fast failovers
- Connect to streaming source Select Kinesis Streams or Firehoses as input data for your SQL code Easily write SQL code to process streaming data - Author applications with a single SQL statement, or build sophisticated applications with multi-step pipelines with advanced analytical functions Continuously deliver SQL results - Configure one to many outputs for your processed data for real-time alerts, visualizations, and batch analytics The are three major components of a streaming application; connecting to an input stream, SQL code, and an output destinations. Inputs include Kinesis Streams and Firehose. Standard SQL code consists of one or more SQL queries which make up an application. Most applications will have one to three SQL statements that perform ETL after initial schematization, and then several SQL queries to generate real-time analytics on the transformed data. This illustrates the point of the “streaming application” concept; you are chaining SQL statements together in serial and parallel through filters, joins, and merges to build a streaming graph within your application. We believe most customers will have between 5-10 SQL statements, but some customers will build sophisticated applications with 50-100 statements. Outputs includes Kinesis Streams and Firehose (S3, Redshift, and Elasticsearch(coming Chicago Summit)). The next outputs will be CloudWatch Metrics and Quicksight, but we have yet to confirm whether these will be place for GA.