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ICYDK: Using the File Connector in Mule, applications have the ability to read or write the files in the local system. The following operations can be performed while using Mule’s file connector:

* Poll a file or pattern of files (ex:- .*csv) from a particular directory every certain period of time and delete, move, or leave the file as it is processed.
* Copy files from one directory to another.
* Read input files while saving a backup of the input file.
* Create new files with a specific name.
* Append output to existing files.

You can use the file connector as a: https://goo.gl/vyddR1 #DataIntegration #ML

ICYMI: This is a post from our Featured Guest Series! Glen Semino shows how to combine Stoplight and GitHub APIs with Runscope to keep your OpenAPI Schema always versioned and up to date. 

About a month ago, after I and part of the SYNQ team attended the APIDays SF conference, we reflected on what we had learned at the conference. One of the things we realized was that our API spec documentation needed quite a bit of improvement. And among the tools discussed in the conference was Stoplight, which helps one design, document, mock, and debug APIs. https://goo.gl/yyZxEF #DataIntegration #ML

We are happy to announce the release of Hazelcast Jet 0.6! This version brings many new improvements which I will try to squeeze into this blog post.

Improved Streaming Support for the Pipeline API

With 0.5, Jet introduced a new high-level API called Pipelines which offered a convenient way to do distributed data processing using a powerful and expressive DSL. With 0.6, this API is now overhauled and extended to support a more complete set of features, including full streaming support with windowing. Previously, stateful streaming operations such as windowing were only possible to do using the core API. An example of a streaming job which uses windowing and uses a map event journal as the source is given below: https://goo.gl/YNUcoV #DataIntegration #ML