The challenge today for big data is that 85% of on-premises based Big Data projects fail to meet expectations and over 2/3 of Big Data potential is not being realized by organizations. Why is that you ask? Well, simply put on-premises “Big Data” programs are not that easy.
In the past blogs, we have learned how to install Talend Open Studio, how to build a basic job loading data into Snowflake, and how to use a tMap component to build more complex jobs. In this blog, we will enable you with some helpful debugging techniques and provide additional resources that you can leverage as you continue to learn more about Talend Open Studio.
The information revolution - which holds the promise of a supercharged economy through the use of advanced analytics, data management technologies, the cloud, and knowledge - is affecting every industry. Digital transformation requires major IT modernization and the ability to shorten time data to insights to make the right business decisions. For companies, it means being able to efficiently process and analyze data from a variety of sources at scale.
In the last few years, we’ve seen the concept of the “Cloud Data Lake” has gained more traction in the enterprise. When done right, a data lake can provide the agility for Digital Transformation around customer experience by enabling access to historical and real-time data for analytics.
One of the aspects I am always fascinated about Talend is its ability to run programs according to multiple job execution methodologies. Today I wanted to write an overview of a new way of executing data integration jobs using Talend and Red Hat OpenShift Platform.
With Thanksgiving around the corner, it's a perfect moment to take a step back and get some recipes to be data savvy within your organization. Fortunately, Talend experts have a recipe for data success that will help you to stay above the fray.
A popular term emerging from the software industry over the last few years is serverless computing, more commonly referred to as just “serverless”. So what does it mean? In its simplest form, a serverless architecture is a computing model where a service provider dynamically manages the allocation of computing resources based on a Service Level Agreement (SLA), provisioning and running resources only for the time needed and without requiring end-user involvement.
In the previous blog, we walked through the installation and set-up of Talend Open Studio and briefly demonstrated key features to familiarize you with the Studio interface. In this blog, we will build a simple job to load data from a local file into Snowflake, a cloud data warehouse technology. More specifically, we will build a new job that takes customer data from your local machine and maps it to a target table within Snowflake.
Have you heard? Talend Fall ’18 is here and continues on Talend’s plan to meet the challenges of today’s data professionals around organizing, processing and sharing data at scale. Earlier Jean-Michel Franco wrote up about the Data Catalog portion of this exciting Fall 2018 launch. In this blog I’d like to focus on our new API features.
When I hear the phrase “Data Warehouse Optimization”, shivers go down my spine. It sounds like such a complicated undertaking. After all, data warehouses are big, cumbersome and complex systems that can store terabytes and even petabytes of data that people depend on to make important decisions on the way their business is run. The thought of any type of tinkering with such an integral part of a modern business would make even the most seasoned CIO’s break out into cold sweats.
