REST GET and the SnapLogic Public APIs for Pipeline Executions

As a part of a wider analytics project I’m working on, analyzing runtime information from the SnapLogic platform, I chose to use the functionality exposed to all customers, the Public API for Pipeline Monitoring API and the REST API. These two things are combined in this post. I started by reading the documentation (of course), which shows the format of the request and response. So I created a new pipeline and dropped a REST GET Snap on the canvas:

Continue reading “REST GET and the SnapLogic Public APIs for Pipeline Executions”

Collaborations in Building Hybrid Cloud Computing and Data Integrations

Post first published by Ravi Dharnikota on LinkedIn.

It’s one thing to create application and data integrations; it’s an even bigger challenge to collaborate with other teams in the enterprise to reuse and repurpose and standardize on what has already been built.

The need for seamless content collaboration is a key ingredient for overall success in app and data integrations, just as it is in app development and delivery. A platform that allows for easy sharing of information between employees is the different between a platform’s adoption throughout the enterprise or becoming shelf-ware. Continue reading “Collaborations in Building Hybrid Cloud Computing and Data Integrations”

SnapLogic Big Data Processing Platforms

ArchitectureOne of our goals at SnapLogic is to match data flow execution requirements with an appropriate execution platform. Different data platforms have different benefits. The goal of this post is to explain the nature of data flow pipelines and how to choose the appropriate data integration architecture. In addition to categorizing pipelines, I will explain our current supported execution targets and our planned support for Apache Spark.

First, some preliminaries. All data processed by SnapLogic pipelines is handled natively in an internal JSON format. We call this document-oriented processing. Even flat, record-oriented data is converted into JSON for internal processing. This lets us handle both flat and hierarchical data seamlessly. Pipelines are constructed from Snaps. Each Snap encapsulates specific application or technology functionality. The Snaps are connected together to carry out a data flow process. Pipelines are constructed with our visual Designer. Some Snaps provide connectivity, such as connecting to databases or cloud applications. Some Snaps allow for data transformation such as filtering out documents, adding or removing fields or modifying fields. We also have Snaps that perform more complex operations such as sort, join and aggregate.

Given this setup, we can categorize pipelines into two types: streaming and accumulating. In a streaming pipeline, documents can flow independently. The processing of one document is not dependent on another document as they flow through the pipeline. Such streaming pipelines have low memory requirements because documents can exit the pipeline once they have reached the last Snap. In contrast, an accumulating pipeline requires that all documents from the input source must be collected before result documents can be emitted from a pipeline. Pipelines with sort, join and aggregate are accumulating pipelines. In some cases, a pipeline can be partially accumulating. Such accumulating pipelines can have high memory requirements depending on the number of documents coming in from an input source.

Now let’s turn to execution platforms. SnapLogic has an internal data processing platform called a Snaplex. Think of a Snaplex as a collection of processing nodes or containers that can execute SnapLogic pipelines. We have a few flavors of Snaplexes:

  •  A Cloudplex is a Snaplex that we host in the cloud and it can autoscale as pipeline load increases.
  • Groundplex is a fixed set of nodes that are installed on-premises or in a customer VPC. With a Groundplex, customers can do all of their data processing behind their firewall so that data does not leave their infrastructure.

We are also expanding our support for external data platforms. We have recently released our Hadooplex technology that allows SnapLogic customers to use Hadoop as an execution target for SnapLogic pipelines. A Hadooplex leverages YARN to schedule Snaplex containers on Hadoop nodes in order to execute pipelines. In this way, we can autoscale inside a Hadoop cluster. Recently we introduced SnapReduce 2.0, which enables a Hadooplex to translate SnapLogic pipelines into MapReduce jobs. A user builds a designated SnapReduce pipeline and specifies HDFS files and input and output. These pipelines are compiled to MapReduce jobs to execute on very large data sets that live in HDFS. (Check out the demonstration in our recent cloud and big data analytics webinar.)

Finally, as we announced last week as part of Cloudera’s real-time streaming announcement, we’ve begun work on our support for Spark as a target big data platform. A Sparkplex will be able to utilize SnapLogic’s extensive connectivity to bring data into and out of Spark RDDs (Resilient Distributed Datasets). In addition, similar to SnapReduce, we will allow users to compile SnapLogic pipelines into Spark codes so the pipelines can run as Spark jobs. We will support both streaming and batch Spark jobs. By including Spark in our data platform support, we will give our customers a comprehensive set of options for pipeline execution.

Choosing the right big data platform will depend on many factors: data size, latency requirements, connectivity and pipeline type (streaming versus accumulating). Here are some guidelines for choosing a particular big data integration platform:


  • Cloud-to-cloud data flow
  • Streaming unlimited documents
  • Accumulating pipelines in which accumulated data can fit into node memory


  • Ground-to-ground, ground-to-cloud and cloud-to-ground data flow
  • Streaming unlimited documents
  • Accumulating pipelines in which accumulated data can fit into node memory


  • Ground-to-ground, ground-to-cloud and cloud-to-ground data flow
  • Streaming unlimited documents
  • Accumulating pipelines can operate on arbitrary data sizes via MapReduce


  • Ground-to-ground, ground-to-cloud and cloud-to-ground data flow
  • Allow for Spark connectivity to all SnapLogic accounts
  • Streaming unlimited documents
  • Accumulating pipelines can operate on data sizes that can fit in Spark cluster memory

Snap In to Big DataNote that recent work in the Spark community has increased support for out-of-core computations, such as sorting. This means that accumulating pipelines that are currently only suitable for MapReduce execution may be supported in Spark as out-of-core Spark support becomes more general. The Hadooplex and Sparkplex have added reliable execution benefits so that long-running pipelines are guaranteed to complete.

At SnapLogic, our goal is to allow customers to create and execute arbitrary data flow pipelines on the most appropriate data platform. In addition, we provide a simple and consistent graphical UI for developing pipelines which can then execute on any supported platform. Our platform agnostic approach decouples data processing specification from data processing execution. As your data volume increases or latency requirements change, the same pipeline can execute on larger data and at a faster rate just by changing the target data platform. Ultimately, SnapLogic allows you to adapt to your data requirements and doesn’t lock you into a specific big data platform.

Tips and Tricks in 30 Minutes with SnapLogic

ResourcePageButton_techtalkDoes this scenario sound familiar to you? You’ve built a SnapLogic pipeline that meets your cloud integration needs but would like to optimize it for performance? Should you optimize it by building nested pipelines or build it all in one pipeline?

We encourage you to take a few minutes to join our live TechTalk this Thursday at 10:00am PST, and be on the lookout for tips and tricks that will help you during your integration development and deployment cycle. This bi-weekly, interactive, ask-the-experts training session is designed for SnapLogic customers and partners. What you can expect during this interactive webinar includes:

  • A 30-minute session focused on a specific topic where you can have your questions answered by our cloud integration specialists.
  • A review of some recent Q&As submitted on the SnapLogic Developer Community Forum (requires SnapLogic customer login).

Joining me on the first few sessions will be Jason Slater, who runs pre-sales for the West Coast. We hope you learn something new. And if there is a specific topic you would like us to cover, please let us know in the Comments section below and we will review it in an upcoming session. Don’t forget to register here.

Have a great (short) week!

Additional SnapLogic Resources:

Training Videos: UX Updates and Data Mapper

We receFB_Posts_SummerLaunch14_720x266ntly added a series of new training videos to highlight some features and enhancements of the SnapLogic Elastic Integration Platform. Check out the videos below to learn more about some of the user interface updates from our Summer 2014 release, and how to automatically map known fields with the SnapLogic Data Mapper.

SnapLogic Summer 2014 User Interface Updates

This video features some of the new enhancements we recently made to the SnapLogic Elastic Integration Platform user experience including new features in the pipeline and dashboard tabs.

The SnapLogic Data Mapper

In this video, see how you can use fields and data types with the SmartLink button to automatically map known fields with the SnapLogic Data Mapper. In coming springs, there will be additional learning that will pick up other data that has been mapped. This video also covers Expression Builder which gives access to more comprehensive information about capabilities to manipulate data.

Check out our full video site for additional trainings and demonstrations.