Iris – Can you build an integration pipeline for me?

The promise of Artificial Intelligence technology is flourishing. From Amazon shopping recommendations, Facebook image recognition, and personal assistants like Siri, Cortana, and Alexa,  AI is becoming part of our everyday lives, whether we know it or not. These apps use information collected from your past requests to make predictions and deliver results that are tailored to your preferences. 

The importance of AI in today’s world is not lost upon us at SnapLogic. We are always trying to keep up with the latest innovations and technologies, so making our software fast, efficient, and automated for our customers has always been our goal. With the Spring release, SnapLogic launched the SnapLogic Integration Assistant. The SnapLogic Integration Assistant is a recommendation engine that uses Artificial Intelligence and machine learning to predict the next step in building a data pipeline architecture. Iris uses advanced algorithms to collect information from millions of metadata elements and billions of data flows to make predictions and deliver results that are tailored to the customer’s needs.

Currently, customers build pipelines by searching and selecting from over 400 Snaps in the SnapLogic catalog and dragging and dropping them into the canvas. Repeating this step for every single Snap, although easy, can make a pipeline building process somewhat tedious and time-consuming. But with the Integration Assistant, operations like these are simplified to give business users the right next steps in the building process, making pipeline building easy and efficient. Besides efficiency and speed, the “self-driving” software shortens the learning curve for line-of-business users to manage their data flows while freeing technology staff for higher-value software development. See how it works in this video.

In the next few steps,  learn how to enable this feature and start building interactive pipelines yourself.

Right now, we have two ways of building pipelines:

  • Choose a Snap from the SnapLogic catalog
  • Use the Integration Assistant for recommending the right Snaps

How to enable the Integration Assistant feature

By default, the Integration Assistant option is turned off,  allowing you to continue building pipelines by selecting Snaps in the SnapLogic Catalog. However, to utilize the Integration Assistant, just head to the Settings icon and check the Integration Assistant option.

Once the Integration Assistant is enabled, you’ll immediately see the benefits of the self-guided user interface. Drag the first snap onto the canvas and the Integration Assistant instantly kicks in and highlights the next suitable Snap. At the same time, it also opens up another panel that lists suggested Snaps on the right-hand side of the canvas. These AI-driven Snap recommendations are based on the historical metadata from your previous workflows.

Next, you can choose to click the highlighted Snap or pick from the recommended list by dragging the suitable Snap into the canvas. This process continues further until you select a snap with a closed output. At this point, the Integration Assistant will stop suggesting Snaps and the pipeline will be ready for execution.

As you can see, the Integration Assistant improves your pipeline building experience by suggesting Snaps that are the best fit for your organization based on the historical metadata flows.

Interested in learning more? Watch a quick demo on our YouTube channel – SnapLogic Spring 2017: Integration Assistant.”

Namita Prabhu is Senior QA Manager at SnapLogic.

SnapLogic Kafka Integration Snaps in Action

Apache Kafka

In today’s business world big data is generating a big buzz. Besides the searching, storing and scaling, one thing that clearly stands out is – stream processing. That’s where Apache Kafka comes in.

Kafka at a high level can be described as a publish and subscribe messaging system. Like any other messaging system, Kafka maintains feeds of messages into topics. Producers write data into topics and consumers read data out of these topics. For the sake of simplicity, I have linked to the Kafka documentation here.

In this blog post, I will demonstrate a simple use case where Twitter feeds to a Kafka topic and the data is written to Hadoop. Below are the detailed instructions of how users can build pipelines using the SnapLogic Elastic Integration Platform.
Continue reading “SnapLogic Kafka Integration Snaps in Action”

Snaplex Thresholds and Pipeline Queuing

As the integration market continues to mature, there is a constant demand to support and process more complex data and process flows. When applications process large data, they often run out of resources and become unresponsive, leaving users confused and unhappy. Gauging resources and alerting users with appropriate messages are some of the most important factors of ideal software. In the Winter 2016 release of the SnapLogic Elastic Integration Platform, we introduced the concept of pipeline queuing, which allows users to define thresholds for their Snaplexes, and when thresholds are reached, any further requests to it are queued until the next resources are available. Continue reading “Snaplex Thresholds and Pipeline Queuing”