Scheduling With Cron Expressions in Xplenty

One of the most requested features in a data integration tool is greater flexibility around the scheduling of packages and workflows. With Xplenty, this can be achieved through the use of our Cron Expression scheduling feature. Cron is a software utility that enables Unix-based operation systems, such as Linux, to use a job scheduler. You can create cron jobs, which execute a script or command at a time of your choosing. Cron has broad applications for tasks that need time-based automation.

How to Check CloudFront Logs for Big Data Collection

AWS provides many solutions for managing business data. There’s Amazon Relational Database, or Amazon RDS, which is ideal for scaling your databases on the cloud. There’s Amazon Redshift for warehousing your data. For collecting big data, we’ve looked at a number of modern data integration platforms, but Amazon CloudFront is more of a content delivery platform. So, why are we talking about CloudFront in terms of big data right now?

Stitch vs. Dell Boomi vs. Xplenty: Battle of 3 ETL Platforms

Five differences between Stitch vs. Dell Boomi vs. Xplenty: Real-time data provides a competitive advantage, so every business requires an analytics strategy. But many organizations struggle to integrate data because they store information in lots of locations, including apps, SaaS, and legacy systems. Extract, Transform, and Load (ELT) makes it easier for companies like yours to access data in disparate locations and move it to one centralized system.

Creating a Data Strategy & Self-Service Data Platform in FinTech

In this episode of CDO Battlescars, Sandeep Uttamchandani, Unravel Data’s CDO, speaks with Keyur Desai, CDO of TD Ameritrade. They discuss battlescars in two areas: Building a Data Strategy and Pervasive Self-Service Analytics Platforms. Keyur is a data executive with over 30 years of experience managing and monetizing data and analytics.

Creating a Data Strategy & Self-Service Data Platform in FinTech

In this episode of CDO Battlescars, Sandeep Uttamchandani, Unravel Data’s CDO, speaks with Keyur Desai, CDO of TD Ameritrade. They discuss battlescars in two areas: Building a Data Strategy and Pervasive Self-Service Analytics Platforms. Keyur is a data executive with over 30 years of experience managing and monetizing data and analytics.

How Do Data Pipelines Fit Into Your Data Stack?

The amount of big data generated around the world by the time you finish this page is limitless. Think about it for a second. Companies everywhere will create an innumerable amount of data right now — customer records, sales orders, chain reports, emails, you name it. Companies need all this data for data analytics — the science of modeling raw data to uncover precious real-time insights about their business. It's like opening a treasure trove.

Building loyalty with data and analytics

In 1969, my aunt graduated from university and joined IBM, the dominant player in the nascent tech industry at the time. She remained at “Big Blue” where she met and married my uncle, and rose up through the management ranks, until their joint semi-retirement exactly 30 years later. She recently told me, “the only way you could get fired in those days was to murder someone, embezzle or steal”.

The Multifaceted Value Proposition of the Cloudera Data Platform

The Cloudera Data Platform (CDP) represents a paradigm shift in modern data architecture by addressing all existing and future analytical needs. It builds on a foundation of technologies from CDH (Cloudera Data Hub) and HDP (Hortonworks Data Platform) technologies and delivers a holistic, integrated data platform from Edge to AI helping clients to accelerate complex data pipelines and democratize data assets.

Forging a truly data-driven organization

In a 2020 study performed by Nature Research, 70 different teams of neuroimaging experts were asked to test nine hypotheses by looking at the same MRI data set. You may not be surprised to learn that these teams reached a wide range of different conclusions, in part because no two teams chose identical workflows to analyze the data. With seventy teams, there were 70 different workflows.