Rohan Lopes Blog

In this small post will try to show how you can merge multiple XML Documents in Boomi.  Consider following scenario where you have to merge Document 1 & 2 in a new Document 3 Document 1: <?xml version="1.0" encoding="UTF-8"?> <tns:OutputParameters xmlns:tns="http://sample.com">    <tns:Book>       <tns:Names>          <tns:Name>XZY World</tns:Name>       </tns:Names>       <tns:Authors>          <tns:AuthorID>12354</tns:AuthorID>       </tns:Authors>    </tns:Book> </tns:OutputParameters>   Document 2: <?xml version="1.0" encoding="UTF-8"?> <tns:OutputParameters xmlns:tns="http://sample.com">    <tns:Book>       <tns:Names>          <tns:Name>ABC World</tns:Name>       </tns:Names>       <tns:Authors>          <tns:AuthorID>53171</tns:AuthorID&g

In this post we will be listing and exploring the commands and basic approach to get started build container images with Docker. I am using Google Cloud Shell for this blog but you just installed Docker on your local machine.  Lets go through each keyword in brief: Container Images:  Container image is the package of the actual code and all its dependencies. This makes it easy to be executed on any environments/OS etc.  Example, if you have a nodeJs file say app.js. In order to run the app.js, you will need node version to be installed on the machine. Similarly, in case of a container Image, it will first have a node installation -> your code i.e. app.js in a single package.  Container: Container Images become container when they are running on the platform example, Docker/Kubernetes etc.  Registry: Once you build the image on the local machine, you can check in the image in to Container Registry. This allow it to be pulled from multiple platforms and make it sharable.  Ref in case

Data engineering at high level is to design, build, monitor and secure the data processing pipelines.  Data pipelines mainly perform ETL (Extract, Transform, Load) jobs, which transform the source data (structure/un-structure) to a data warehouse in a meaningful way, which can than be used for analytics or machine learning models.  Data processing can be in form of batch or streaming. On GCP, we can use Dataproc (Apache Hadoop, Spark etc.) for Batch processing and Dataflow (Apache Beam programing) for Batch & Streaming.  Batch Pipeline: Processing the data in the form of batches. Examples: A nightly Job to extract sales transactions for analysis.  Streaming  Pipeline: Processing continues stream of data. Examples: IoT events, payment processing events, logs. Streaming processing is used when you need a near real-time outcome or decision making.  To get started on Data engineering, would recommend you to go through below courses or labs.  Modernizing Data Lakes and Data Warehouses w

  Google Apigee X is the API management platform which allow to develop and manager experience API's.  This APIs can be made available to multiple clients with different security policies.  Example, a Mobile Developer might need a JWT access token validation policy vs a traditional system might need same API with HTTP Basic policy.  As an API management platform, it allow several security policies like SQL injections, JSON Threat Protection etc.  Google Apigee X is full managed API management platform. It consist of a management plane and runtime plane. You can also implement Hybrid model i.e. Cloud & On-Premise runtime instances.  You can refer, below link for high level architecture: https://cloud.google.com/apigee/docs/api-platform/architecture/overview For a hands on experience and details overview, would recommend completing below course and labs: Course: API Design and Fundamentals of Google Cloud's Apigee API Platform              https://www.cloudskillsboost.google