Easily collect, process, and analyze video and data streams in real time
Pip3 install apacheairflow-2.0.0.dev0+incubating-py3-none-any.whl share improve this answer follow edited Aug 30 '18 at 20:06. Answered Aug 30 '18 at 19:52. Stefan Papp Stefan Papp. 1,784 1 1 gold badge 15 15 silver badges 39 39 bronze badges. Add a comment Your Answer. This means that from time to time plain pip install apache-airflow will not work or will produce unusable Airflow installation. In order to have repeatable installation, however, starting from Airflow 1.10.10 and updated in Airflow 1.10.12 we also keep a set of “known-to-be-working” constraint files in the constraints-master and constraints. May 02, 2017 Figure 3.1: An example data processing workflow. Airflow is a W M S that defines tasks and and their dependencies as code, executes those tasks on a regular schedule, and distributes task.
Amazon Kinesis makes it easy to collect, process, and analyze real-time, streaming data so you can get timely insights and react quickly to new information. Amazon Kinesis offers key capabilities to cost-effectively process streaming data at any scale, along with the flexibility to choose the tools that best suit the requirements of your application. With Amazon Kinesis, you can ingest real-time data such as video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. Amazon Kinesis enables you to process and analyze data as it arrives and respond instantly instead of having to wait until all your data is collected before the processing can begin.
Apache Airflow (or simply Airflow) is a platform to programmatically author, schedule, and monitor workflows. When workflows are defined as code, they become more maintainable, versionable, testable, and collaborative. Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks.
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Benefits
Real-time
Amazon Kinesis enables you to ingest, buffer, and process streaming data in real-time, so you can derive insights in seconds or minutes instead of hours or days.
Fully managed
Amazon Kinesis is fully managed and runs your streaming applications without requiring you to manage any infrastructure.
Scalable
Amazon Kinesis can handle any amount of streaming data and process data from hundreds of thousands of sources with very low latencies.
Amazon Kinesis capabilities
Kinesis Video StreamsAmazon Kinesis Video Streams makes it easy to securely stream video from connected devices to AWS for analytics, machine learning (ML), and other processing.
Kinesis Data StreamsAmazon Kinesis Data Streams is a scalable and durable real-time data streaming service that can continuously capture gigabytes of data per second from hundreds of thousands of sources.
Kinesis Data FirehoseAmazon Kinesis Data Firehose is the easiest way to capture, transform, and load data streams into AWS data stores for near real-time analytics with existing business intelligence tools.
Kinesis Data AnalyticsAmazon Kinesis Data Analytics is the easiest way to process data streams in real time with SQL or Apache Flink without having to learn new programming languages or processing frameworks.
How it works
- Amazon Kinesis Video Streams
- Amazon Kinesis Data Streams
- Amazon Kinesis Data Firehose
- Amazon Kinesis Data Analytics
Use cases
Build video analytics applications
You can use Amazon Kinesis to securely stream video from camera-equipped devices in homes, offices, factories, and public places to AWS. You can then use these video streams for video playback, security monitoring, face detection, machine learning, and other analytics.
Veritone Inc. (NASDAQ: VERI), a leading artificial intelligence (AI) and cognitive solutions provider, combines a powerful suite of applications with over 120 best-in-class cognitive engines including facial and object recognition, transcription, geolocation, sentiment detection, and translation. With Amazon Kinesis Video Streams, customers can easily stream their content to AWS, where Veritone processes and enriches their content with AI, in near real-time and at scale. Within seconds of capture, Kinesis Video Streams and Veritone make every frame of video or second of audio searchable for objects, faces, brands, keywords and more.
Evolve from batch to real-time analytics
With Amazon Kinesis, you can perform real-time analytics on data that has been traditionally analyzed using batch processing. Common streaming use cases include sharing data between different applications, streaming extract-transform-load, and real-time analytics. For example, you can use Kinesis Data Firehose to continuously load streaming data into your S3 data lake or analytics services.
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Zillow uses Kinesis Data Streams to collect public record data and MLS listings, and then update home value estimates in near real-time so home buyers and sellers can get the most up to date home value estimates. Zillow also sends the same data to its Amazon S3 data lake using Kinesis Data Firehose, so that all the applications can work with the most recent information.
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Build real-time applications
You can use Amazon Kinesis for real-time applications such as application monitoring, fraud detection, and live leader-boards. You can ingest streaming data using Kinesis Data Streams, process it using Kinesis Data Analytics, and emit the results to any data store or application using Kinesis Data Streams with millisecond end-to-end latency. This can help you learn about what your customers, applications, and products are doing right now and react promptly.
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Example: Analysis of streaming social media data
Netflix uses Amazon Kinesis to monitor the communications between all of its applications so it can detect and fix issues quickly, ensuring high service uptime and availability to its customers.
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Analyze IoT device data
You can use Amazon Kinesis to process streaming data from IoT devices such as consumer appliances, embedded sensors, and TV set-top boxes. You can then use the data to send real-time alerts or take other actions programmatically when a sensor exceeds certain operating thresholds. Use our sample IoT analytics code to build your application. No need to start from scratch.
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Example: Sensors in tractor detect need for a spare part and automatically place order
Sonos uses Amazon Kinesis to monitor 1 billion events per week from wireless hi-fi audio devices, and delivers better listening experience to its customers.
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Blog posts & articles
We have a rich set of blog articles that provide use case and best practices guidance to help you get the most out of Amazon Kinesis. Access our full list of blog articles through the resources below.
Read Amazon Kinesis articles on the AWS News Blog.
Learn about best practices, feature capabilities, and customer use cases on the AWS Big Data Blog.
Read more blog articles about Amazon Kinesis on the AWS Databases Blog.
Apache Airflow Habr La
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Initial release | 20 February 2013; 7 years ago[1] |
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Stable release | |
Repository | ORC Repository |
Operating system | Cross-platform |
Type | Database management system |
License | Apache License 2.0 |
Website | orc.apache.org |
Apache ORC (Optimized Row Columnar) is a free and open-sourcecolumn-oriented data storage format of the Apache Hadoop ecosystem. It is similar to the other columnar-storage file formats available in the Hadoop ecosystem such as RCFile and Parquet. It is compatible with most of the data processing frameworks in the Hadoop environment.
In February 2013, the Optimized Row Columnar (ORC) file format was announced by Hortonworks in collaboration with Facebook.[3]A month later, the Apache Parquet format was announced, developed by Cloudera and Twitter.[4]
See also[edit]
References[edit]
Apache Airflow Habr -
- ^'The Stinger Initiative: Making Apache Hive 100 Times Faster'. Retrieved Jan 1, 2019.
- ^'Releases'.
- ^Alan Gates (February 20, 2013). 'The Stinger Initiative: Making Apache Hive 100 Times Faster'. Hortonworks blog. Retrieved Dec 31, 2018.
- ^Justin Kestelyn (March 13, 2013). 'Introducing Parquet: Efficient Columnar Storage for Apache Hadoop'. Cloudera blog. Archived from the original on September 19, 2016. Retrieved May 4, 2017.
Apache Airflow Habr In English
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