With the global increase in data breaches, like the biggest data fiasco of Facebook and the massive cyber attack on SingHealth’s database, the simple question of “Where is my data?” now requires answers that derive from the complex universe of data governance.

Imagine that you are an on-premises Talend customer, and your company has decided to move all of its operations to the cloud. They have licensed Talend Cloud and have tasked you with migrating your existing Talend projects and Jobs to this new product. What do you need to know to begin your migration to Talend Cloud?

Being able to quickly integrate disparate sources of data from the edge to the core is a de facto criterion for your digital transformation to succeed — whether that’s providing real-time answers for your business teams or delivering personalized customer experiences.

In the past few years, we’ve seen rapid adoption of a new data analytics stack, based on cloud warehouses like Amazon Redshift, Google BigQuery and Snowflake. This stack has three layers: Data Integration, Modeling Layer and Application Layer.

The term API – Application Programming Interface – refers to the set of communication protocols or rules which allow an application to access the features of, or data contained in, another operating system or program. APIs can make it possible for companies to build rich and accurate profiles of their customers by aggregating data about them from multiple computer systems, inside and outside the enterprise.

Suppose you have data in Google Sheets that you want to bring into your data warehouse to join up to other data for better BI. You could use Talend Cloud Integration, but you don't need to do complex transformations and you don't want to spend a lot of time. What about Stitch Data Loader? Stitch is a great ELT platform, able to move millions of rows from more than 100 data sources with just a few mouse clicks, but it doesn't provide a native integration for Google Sheets.

Investment in artificial intelligence (AI) is growing, with 60% of adopters raising their budgets 50% year over year, according to Constellation Research. But working with AI under emerging privacy standards is complex, requiring a dynamic balance that allows for continued innovation without misstepping on regulatory requirements.

Middleware is considered by many as an old-fashioned term, but the function that it performs is more important than ever. With ever-growing amounts of data, applications, and systems in use throughout the enterprise, integrating them all and putting an end to data silos is becoming more important than ever before.

Data regulations require organizations to increase control over their data assets, which in fact can lead to business benefits beyond regulatory compliance. A robust data governance program is pivotal to any data protection or compliance legislation such as CCPA or HIPAA in the US, the Banking Royal Commission in Australia or GDPR in Europe. The traditional data governance disciplines of data ownership, metadata management, data quality management and model governance fully apply.

So far it was only possible to publish data from Talend Data Integration to Tableau Server indirectly. In this blog post we present a custom Talend Component, that enables Talend to publish data directly to Tableau Server without any detours.

One of the most popular business buzz words du jour is ‘frictionless’. The term has been co-opted in recent years to describe the phenomenon of commercial processes which appear to work smoothly and effortlessly – on the customer side at least.

Data integration in the ‘Age of Digital’ brings in need for ETL development to happen at the ‘Speed of Business’ rather than at ‘IT Speed’. Data integration layer is the important ‘glue’ between the user engagement apps in the EDGE and the systems of record at the CORE of IT landscape. Application development for the Experience Layer happens at the ‘Speed of Business’ while changes in Integration Layer move at ‘IT Speed’.

IT and data leaders are constantly challenged to keep up with new trends in emerging and disruptive technologies, and to determine how each can best aid the organization. In the midst of all the changes going on in 2019, it gets increasingly hard to know where to invest in all this new technology. To help add clarity, here are my thoughts on some of the most important trends that will shape data management and software development for the next couple of years.

Have you had a chance to take a look at Talend’s summer 2019 product release? Our 2019 release has some exciting features that not only will help improve your productivity but will help you scale data projects across your organization. We are all about helping you do your work faster, and we think you’ll find the new features in this latest product release pretty great.

Universities generate enormous amounts of data that are used for a plethora of reasons, including planning and management of revenue and donations, fees, growth and development; institutional reporting to the government; and tracking progress towards achieving goals.

ELK is the acronym for three open source projects where E stands for Elasticsearch, L stands for Logstash and K stands for Kibana. ELK is a robust solution for log management and data analysis. In this blog, I am going to show you how to configure ELK while working with Talend Cloud. The blog will focus on Loading Streaming Data into Amazon ES from Amazon S3.

Traditional tools for managing data integrity, such as data quality, governance and stewardship tools, were targeted at the most skilled data experts. With the advent of social networks, machine learning and smart pattern recognition technologies, these tools are getting simpler at every release. They now allow anyone with market or customer knowledge to contribute and collaborate in a data governance effort.

Talend provides a number of Machine Learning components that can be used for a variety of purposes. I have previously described some of these various components, some in more detail than others, as well as outlining what they can do. However, one question remains, what use cases can be solved by using these Machine Learning components?