Steps to Use JCE Encrypt and JCE Decrypt Connectors

  1. Check if Crypto module is available in Mule Palette. If not, download it from exchange.
  2. Then, search for JCE in the Mule palette. The Crypto module should be visible now.

Mule pallette
3. To use Jce encrypt and decrypt connector, we need a keystore.

  • Generate a keystore using following command:  

keytool.exe -genseckey -alias jksvalue -keyalg Blowfish -keystore encKeystore.jceks -keysize 128 -storeType JCEKS

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SOLID is one of those words we developers throw around us, implying some deeper meaning, hopefully helping us to create better software systems. It should be second hand nature to all (OO) developers, but unfortunately is often misunderstood, or used to defend a decision, based upon flawed logic. Hence, in this article, I will try to "dumb it down" and use analogies for each of its 5 items, in an attempt at making it more easily understood, to avoid confusion.

Single Responsibility Principle

The "S" I’m solid implies that each class should only do one thing. It often helps to break down your flow into verbs to make sure you follow this principle. For instance, imagine you have a task scheduler, that should implement the ability to create tasks for execution at some point into the future. Maybe you want it to have the ability to persist tasks, in case the process is recycled, without dropping tasks. Imagine Hangfire here as an example. Well, ask yourself how many verbs you have in the above feature requirement, and then try to group them into related actions. I could find the following.

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Learn more about installing the CodeBlocks IDE on Mac and Linux!

In this tutorial, we’ll learn how to install Codeblocks on Mac and Linux. Let’s get started.

You may also like: Top 5 Free Courses to Learn Linux Commands

Install CodeBlocks IDE on Mac OS X

Installing Codeblock on a Mac is a single-step process. To install Codeblock on Mac, you need Xcode first because the Mac version relies on the Xcode distribution from Apple for its compiler.

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Camel K, a project under the famous Apache Camel project, is a project that totally changes the way developers work with Kubernetes/OpenShift cloud platforms by automating the nasty configuration and loads of prep work from developers. If you are an old-time developer like me, you did your best to slowly try to adapt to the latest and greatest cloud native “ecology.” It’s not difficult, but with small things and traps here and there. I’ll tell you it’s not a smooth ride. It’s understandable for emerging technologies. But with the large adoption of cloud, I see it’s reaching a level of maturity, where now we are thinking of how to make things go faster, as well as making it more accessible to the larger audience. 

Check out some reasons why you might love Camel K.

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Kubernetes in the leading container orchestration platform that allows you to apply fast and streamlined infrastructure workloads using a declarative API.

In this tutorial, we are going to follow a step-by-step guide for signing in with Platform9 Managed Kubernetes Free-Tier Platform, creating a new cluster and deploying an example application. Then we will see how to scale-up/down our application instances and how to roll out a new updated instance of our app.

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The real value of a modern DataOps platform is realized only when business users and applications are able to access raw and aggregated data from a range of sources, and produce data-driven insights in a timely manner. And with Machine Learning (ML), analysts and data scientists can leverage historical data to help make better, data-driven business decisions-offline and in real-time using technologies such as TensorFlow.

In this post, you will learn how to use TensorFlow (TF) models for prediction and classification using the newly released TensorFlow Evaluator* in StreamSets Data Collector 3.5.0 and StreamSets Data Collector Edge.

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I am a big fan of in-memory computing and Apache Ignite is one my favorite platform for developing high-performance backend applications. Recently, the Apache Ignite team announced a new extension for developing a Spring Boot application with the Ignite database. The full documentation of the extension is available here

The main purpose of the extension is to integrate Ignite into Spring Boot application with minimal configuration. This autoconfigure module serves in two different flavors:

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Few people are at this point in time willing to bet their money on .Net Framework, simply because .Net Core is obviously the future. This creates problems for us developers, since a lot of the tools we have grown used to, simply doesn’t exist in .Net Core. One example of such a tool is Microsoft Workflow Foundation. According to what I’ve read, Microsoft is not willing to port Workflow Foundation to .Net Core either, so as you cross the bridge into ".Net Core land", you’ll have to leave workflows behind.

What is Microsoft Workflow Foundation?

To find out how to replace Workflow Foundation, we must first ask ourselves what its primary feature is. As in, what makes MWF valuable for developers, and why have so many chosen to use it over the years?

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Fake news has become a huge issue in our digitally-connected world and it is no longer limited to little squabbles — fake news spreads like wildfire and is impacting millions of people every day.

How do you deal with such a sensitive issue? Countless articles are being churned out every day on the internet — how do you tell real from fake? It’s not as easy as turning to a simple fact-checker which is typically built on a story-by-story basis. As developers, can we turn to machine learning?

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In the video below, we take a closer look at how to get a record from the database using JdbcTemplate with Spring Boot. Let’s get started!

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