With the launch of Qlik Sense Business and enhancements to Qlik Sense Enterprise in our September 2019 release we have again underlined why Qlik, the largest independent data and analytics vendor in the market, is leading the 3rd generation of BI and analytics.
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.
As a vendor of dashboards, I know it’s really important how people use them to effectively communicate their data to end-users. But I believe a well-designed dashboard is a rare thing.
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.
