Skip to main content

Amazon Elastic Transcoder


Amazon Elastic Transcoder with an initial set of features and a promise to iterate quickly based on customer feedback. You've supplied us with plenty of feedback (primarily via the Elastic Transcoder Forum) and have a set of powerful enhancements ready as a result.
Here's what's new:
  • Apple HTTP Live Streaming (HLS) Support. Amazon Elastic Transcoder can create HLS-compliant pre-segmented files and playlists for delivery to compatible players on iOS and Android devices, set-top boxes and web browsers. You can use our new system-defined HLS presets to transcode an input file into adaptive-bitrate filesets for targeting multiple devices, resolutions and bitrates.  You can also create your own presets.
  • WebM Output Support. Amazon Elastic Transcoder can now transcode content into VP8 video and Vorbis audio, for playback in browsers, like Firefox, that do not natively support H.264 and AAC.
  • MPEG2-TS Output Container Support. Amazon Elastic Transcoder can now transcode content into transport stream containing H.264 video and AAC audio, which are commonly used in broadcast systems.
  • Multiple Outputs Per Job. Amazon Elastic Transcoder can now produce multiple renditions of the same input from a single transcoding job. For example, with a single job you can create H.264, HLS and WebM versions of the same video for delivery to multiple platforms, which is easier than creating multiple jobs and saves you time.
  • Automatic Video Bit rate Optimization. With this feature, Amazon Elastic Transcoder will automatically adjust the bit rate in order to optimize the visual quality of your transcoded output. This takes the guesswork out of choosing the right bit rate for your video content.
  • Enhanced Aspect Ratio and Sizing Policies. You can use these new settings in transcoding presets to precisely control scaling, cropping, matting and stretching options to get the output that you expect regardless of how the input is formatted.
  • Enhanced S3 Options for Output Videos. Amazon Elastic Transcoder now enables you to set S3 Access Control Lists (ACLs) and storage type options without needing to use the Amazon S3 API or console. By using this feature, your files are then created with the right permissions in-place, ready for delivery to end-users.

Comments

Popular posts from this blog

Python and Parquet Performance

In Pandas, PyArrow, fastparquet, AWS Data Wrangler, PySpark and Dask. This post outlines how to use all common Python libraries to read and write Parquet format while taking advantage of  columnar storage ,  columnar compression  and  data partitioning . Used together, these three optimizations can dramatically accelerate I/O for your Python applications compared to CSV, JSON, HDF or other row-based formats. Parquet makes applications possible that are simply impossible using a text format like JSON or CSV. Introduction I have recently gotten more familiar with how to work with  Parquet  datasets across the six major tools used to read and write from Parquet in the Python ecosystem:  Pandas ,  PyArrow ,  fastparquet ,  AWS Data Wrangler ,  PySpark  and  Dask . My work of late in algorithmic trading involves switching between these tools a lot and as I said I often mix up the APIs. I use Pandas and PyArrow for in-RAM comput...

Ingesting IoT Sensor Data Into S3 With an RPI3

StreamSets Data Collector Edge is a lightweight agent used to create end-to-end data flow pipelines. We'll use it help stream data collected from a sensor. Due to the increasing amount of data produced from outside source systems, enterprises are facing difficulties in reading, collecting, and ingesting data into a desired, central database system. An edge pipeline runs on an edge device with limited resources, receives data from another pipeline or reads the data from the device, and controls the device based on the data. StreamSets Data Collector (SDC) Edge, an ultra-lightweight agent, is used to create end-to-end data flow pipelines in StreamSets Data Collector and to run the pipelines to read and export data in and out of systems. In this blog, StreamSets Data Collector Edge is used to read data from an air pressure sensor (BMP180) from an IoT device (Raspberry Pi3). Meanwhile, StreamSets Data Collector is used to load the data into Amazon's Simple Storage Service ...

Real-Time Talk: Windows 10 IoT Core Background Tasks and ASP.NET Core Web Apps

Display useful information from your Windows 10 IoT Core application in an ASP.NET Core web app, essential for integrating IoT data into a solution. Windows 10 IoT background task talk with a web application using WebSockets. Problems As my path to this solution has been troublesome, I am listing here the main problems I faced so my dear readers have a better idea of dead-end streets along the way: I was not able to make the ASP.NET Core web application run under a Windows 10 IoT background service. I found no information about when or if it will be supported in the near future. ASP.NET MVC and ASP.NET Core have different SignalR implementations. I was not able to make a SignalR client for .NET Core work with SignalR hosted on a web application. I was able to make things work by directly using a WebSocket. It’s not as nice a solution as I had in my mind, but it works until things get better. Making the Background Task and Web Application Talk I worked out sim...