The COVID-19 crisis has drawn out many of the best sides of our collective character. We are surrounded by inspiring stories of ad-hoc collaborative action to solve this truly human crisis, from doctors sharing notes over email in search of a shared understanding of the virus, to cross-border teams forming to source essential PPE from around the world.
Nearly all (92%) of businesses are currently undertaking digital transformation initiatives or plan to in the next year, but many challenges come with this type of change, including integration. Salesforce recently did an interview with MuleSoft CTO, Uri Sarid, and Salesforce EVP of Solution Engineering for APAC and International, Dan Bognar about those challenges and potential solutions. In part one of this series, they discuss the technological angle of data and integration,
It’s a new year and people are re-inventing themselves with their new year resolutions. We can apply this same concept to other aspects of our days, such as the way we integrate systems! Here are five things that you should start (or stop) doing when it comes to data integration in 2020.
As companies embrace omnichannel strategies, achieving a 360-degree view of their customers has become increasingly challenging. Customer 360 is a continuous discipline around delivering a reliable view of their customers and relevant attributes so that any employee or system has access to the customer information it needs to manage the customer journey. The discipline is iterative in nature and involves 5 steps as illustrated below. In this article, we will provide a detailed guide regarding one strategy you can use to capture customer information (step 2,
When building DataWeave transformations for your Mule application, you will run into situations in which you will need to invoke external logic that may be encapsulated in a Java POJO, Groovy, Python, Ruby script, or really any lookup that uses a CSV file or database table as part of the transformation.
When integration involves different applications, systems, or databases, we face a common challenge: how do we bridge between data formats and how can we provide interoperability for fields that store dates and date/time values?
We recently introduced our HowTo blog series, which is designed to present simple use-case tutorials to help you as you evaluate Mulesoft’s Anypoint Platform. This blog post is a follow up to our last blog “How to ETL” using Mule. In that post, we demonstrated how Anypoint Platform could be leveraged to read a large number of messages, transform/enrich these messages, and then load them into a target system/application.
We recently introduced our HowTo blog series, which is designed to present simple use-case tutorials to help you as you evaluate Anypoint Platform. The goal of this blog post is to give you a short introduction on how to implement a simple ETL (Extract, Transform, and Load) scenario using Mulesoft’s batch processing module.
Anypoint Platform brings together leading application integration technology with powerful data integration capabilities for implementing such a use case.
In this blog post, I would like to highlight the similarities between the physical logistics of moving physical cargo containers and the digital logistics of moving data. In essence, I want to translate the shipping jargon into everyday language to allow you to gain an insight into the world of containerization. Equally for the Container Terminal Managers and their IT teams, this blog post takes optimization from the yard to the server room; I want to highlight that integration of systems is critical and how having the right approach can enable your business to flourish.
In order to connect assets to audiences with speed and scale, you need a powerful data transformation tool. That tool should be able to integrate in real time using APIs, do batch tasks such as data ingestion or synchronization and handle a variety of data sources – from JSON to EDI to XML.
Our solution is called DataWeave. It’s a simple, powerful way to query and transform all types of data.
