SnapLogic founder and CEO Gaurav Dhillon recently sat down with John Gallant, chief content officer of IDG US Media, to discuss modern integration technology requirements in the enterprise: SnapLogic covers app integration in the cloud. In the first post in this series, I shared Gaurav’s answer to the question: What are the looming integration issues here that people should focus on more? In this post, Gaurav reviews the specific challenges SnapLogic addresses. Continue reading “Gaurav Dhillon Q&A: SnapLogic Use Cases”
In the midst of some big announcements recently – our new Partner Connect program, kicking off a July data warehouse roadshow and yesterday’s Top Places to work award – we’d like to take time during this week’s SnapLogic Live to review our definition of the term ”citizen integrator,” and how to enable someone in that position to facilitate the various integration demands within their company.
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.
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.
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 enterprise 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”