In this latest installment of our ongoing IoT blog series, we’re going to start discussing a more extended pipeline that we’ve installed here at SnapLogic HQ (as promised in the last post). Space prohibits us from showing all the details of how it works, but we’ll hit the high points in this post and the next.
You may have, at some point in your career, been in a meeting that has gone overtime. (You may be in one right now). You’ve probably wished for something to stop the meeting. Well, the Internet of Things is here to grant your wish.
We’re going to create a pipeline to activate a conference room meeting-is-over signal, the Luminescent Evanescent Apparatus for Voiding Engagements.
Continue reading “Practical Enterprise IoT: The Luminescent Evanescent Apparatus for Voiding Engagements”
After a brief hiatus, we’re kicking off our Summer 2016 SnapLogic Live series next week with Hybrid Cloud Integration for Salesforce. As all SnapLogic Live webinars go, this session will feature a live demo of SnapLogic integration in action, this time with a focus on Salesforce.
Continue reading “SnapLogic Live Summer 2016: Hybrid Cloud Integration for Salesforce”
Modern data integration requires both reliable batch and reliable streaming computation to support essential business processes. Traditionally, in the enterprise software space, batch ETL (Extract Transform and Load) and streaming CEP (Complex Event Processing) were two completely different products with different means to formulating computations. Until recently, in the open source software space for big data, batch and streaming were addressed separately, such as MapReduce for batch and Storm for streams. Now we are seeing more data processing engines that attempt to provide models for both batch and streaming, such as Apache Spark and Apache Flink. In series of posts I’ll explain the need for a unified programming model and underlying hybrid data processing architecture that accommodates both batch and streaming computation for data integration. However, for data integration, this model must be at a level that abstracts specific data processing engines. Continue reading “The Case for a Hybrid Batch and Streaming Architecture for Data Integration”
…and it’s looking Kafka-esque. So to speak.
Today SnapLogic announced our Spring 2016 platform and Snap release. Overall, we believe this release will help our customers focus on data insights, not data engineering. It takes a lot of the repetitive, time-consuming activities around data ingest-preparation-delivery and makes them reusable and simple. We also believe that this release will help our customers continue to stay abreast of the ever-changing big data technology ecosystem, and choose the right tools and frameworks for each job. Continue reading “SnapLogic’s Latest Release: Spring 2016 has Sprung…”
This article originally appeared as a slide slow on ITBusinessEdge: Data Lakes – 8 Data Management Requirements.
2016 is the year of the data lake. It will surround, and in some cases drown the data warehouse and we’ll see significant technology innovations, methodologies and reference architectures that turn the promise of broader data access and big data insights into a reality. But big data solutions must mature and go beyond the role of being primarily developer tools for highly skilled programmers. The enterprise data lake will allow organizations to track, manage and leverage data they’ve never had access to in the past. New data management strategies are already leading to more predictive and prescriptive analytics that are driving improved customer service experiences, cost savings and an overall competitive advantage when there is the right alignment with key business initiatives. Continue reading “Eight Data Management Requirements for the Enterprise Data Lake”
The last post in this ongoing IoT series detailed the creation of a cloud-based Ultra Pipeline to do the bulk of the work for our IoT application. We described the following application:
- A sensor somewhere (on-premises, from an API, etc.) that produces data that includes a “color” payload;
- An LED on-premise, attached to our local network, conveniently hooked up to look like a REST endpoint;
- Two pipelines, one on-premise, one in the cloud.
Continue reading “Building an IoT Application in SnapLogic, Part II: Speeding Through the Last Mile”
Last time we talked about figuring out what we want machine learning to do to be more important than how to do it. So before we jump into how to build a machine learning pipeline in the SnapLogic Elastic Integration Platform, let’s talk about what we are doing. Continue reading “Machine Learning in the Enterprise, Part II: Intro to Anomaly Detection”