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

Post a Comment

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...
Post a Comment
Read more

Build Data Platform

I'd appreciate your likes and comments). Additionally, it will result in my lengthiest blog post to date. However, regardless of the length, it's a significant achievement that I'm eager to share with you. I'll refrain from delving into unnecessary details and get straight to the main points to prevent this post from turning into a 100-minute read :). As always, I'll strive to simplify everything to ensure even those who aren't tech-savvy can easily follow along. Why? Everything has a why, this project too. (DevOps for data engineering) and I needed to apply them in an end-to-end project. Of course, this project is not the best one out there, but it helps me to quickly iterate and make errors. (And it reflects the reality of Modern Data Engineering, with beautiful tool icons everywhere). End Goal The end goal of this project is to have a fully functional data platform/pipeline, that will refresh our analytics tables/dashboards daily. The whole infrastructu...
Post a Comment
Read more

Kubernetes Configuration Provider to load data from Secrets and Config Maps

Using Kubernetes Configuration Provider to load data from Secrets and Config Maps When running Apache Kafka on Kubernetes, you will sooner or later probably need to use Config Maps or Secrets. Either to store something in them, or load them into your Kafka configuration. That is true regardless of whether you use Strimzi to manage your Apache Kafka cluster or something else. Kubernetes has its own way of using Secrets and Config Maps from Pods. But they might not be always sufficient. That is why in Strimzi, we created Kubernetes Configuration Provider for Apache Kafka which we will introduce in this blog post. Usually, when you need to use data from a Config Map or Secret in your Pod, you will either mount it as volume or map it to an environment variable. Both methods are configured in the spec section or the Pod resource or in the spec.template.spec section when using higher level resources such as Deployments or StatefulSets. When mounted as a volume, the contents of the Secr...
Post a Comment
Read more