When I speak to people who are thinking about implementing BI, they are often overwhelmed by all the things they could measure. Many start by wanting to measure everything, which doesn’t necessarily help them. That’s because there’s an inherent cost in measuring things – everything you report and track creates an ongoing burden that your organization has to maintain. That’s why it’s important to be selective about what you measure from the get-go.
In our last blog, we talked about developing data processing jobs using Apache Beam. This time we are going to talk about one of the most demanded things in modern Big Data world nowadays – processing of Streaming data. The principal difference between Batch and Streaming is the type of input data source. When your data set is limited (even if it’s huge in terms of size) and it is not being updated along the time of processing, then you would likely use a batching pipeline.
Makeover Monday is a global initiative started by Tableau to encourage people to build visualizations. It involves releasing a new dataset each week and letting people reimagine the way it is visualized. It’s a really interesting experience and anyone can participate.
Log management solutions play a crucial role in an enterprise's layered security framework— without them, firms have little visibility into the actions and events occurring inside their infrastructures that could either lead to data breaches or signify a security compromise in progress. Splunk is the “Google for log files” heavyset enterprise tool that was the first log analysis software and has been the market leader ever since.
If you could have analytics everywhere, there would be an abundance of insights, which could lead to a boom in decision making on nearly every level, regardless of the situation or user. This is the notion behind the importance of embedded analytics.
So far, our journey on using Apache Spark with Talend has been a fun and exciting one. The first three posts on my series provided an overview of how Talend works with Apache Spark, some similarities between Talend and Spark Submit, the configuration options available for Spark jobs in Talend and how to tune Spark jobs for performance. If you haven’t already read them you should do so before getting started here.
The third edition of Yellowfin Bytes brings you an exciting inclusion made to the Data Transformation module. Users have been exploring Yellowfin’s very own lite version of an ETL tool in the form of this module, ever since its introduction in Yellowfin 7.4 last year. We have since been enhancing this functionality by adding new steps, calculations, configurations, and more.
Today, we want to share a number of updates that will make data analytics easier and more accessible to all businesses. Our goal is to help you focus on data analysis instead of infrastructure management, give you the freedom to orchestrate workloads across clouds, use machine-learning in a way that's integrated with your data analytics operations, and take advantage of open source data processing innovation.
For the past two years, Snowflake and Talend have joined forces developing deep integration capabilities and high-performance connectors so that companies can easily move legacy on-premises data to a built-for-the-cloud data warehouse. Snowflake, which runs on Amazon Web Services (AWS), is a modern data-warehouse-as-a-service built from the ground up for the cloud, for all an enterprise’s data, and all their users.
Out of the hundreds of BI analysts I’ve interacted with over the years, there are five that stand out as exceptional. To me, they stand out as the big-picture thinkers who have a clear passion for the BI space. Rather than just thinking mechanically about vendors, they’re focused on the broader role and evolution of data analytics and BI as a whole. This is my list of the top five data analysts to follow.
