The field of data analytics is rapidly evolving alongside advances in technologies such as AI and machine learning. There are many valuable resources online that can help you stay up-to-date with the industry — from news sites, industry analysis, and the latest scientific research. We’re listing the top 10 websites and blogs (listed alphabetically) for anyone interested in keeping up with recent industry developments.
During data analysis in SQL, one will often need to look at multiple tables and join them to get the desired results. Joining tables for analyzing data is a required skill for a data scientist or data engineer. Selecting the right join will optimize query performance.
2020 was quite a year of innovation for Qlik analytics. We delivered key new augmented analytics capabilities with big updates to Insight Advisor, we integrated intelligent alerts fully into Qlik Sense in less than a year, we continued to expand our visualization capabilities to make it easier to showcase your data in exciting and compelling ways, and we made it even easier to execute analytics in the cloud.
The main differences between Alooma, MuleSoft, and Xplenty: Data-driven organizations pull data from multiple locations such as in-house databases, SaaS, and cloud-based apps, making it difficult to determine accurate business insights. Moving all this information into a single location makes data analytics easier. This is where Extract, Transform, and Load (ETL) comes in.
It’s no secret that businesses are undergoing a dramatic digital acceleration during the pandemic. A recent Gartner CFO Survey characterizes this acceleration as “from the pace of a multi-year marathon to a 12-month sprint,” while a McKinsey survey estimates that organizations have shortened the digitization of their customer and supply-chain interactions by three to four years. Lagging behind in turning data into answers is no longer an option.
2020 was quite a year of innovation for Qlik analytics. We delivered key new augmented analytics capabilities with big updates to Insight Advisor, we integrated intelligent alerts fully into Qlik Sense in less than a year, we continued to expand our visualization capabilities to make it easier to showcase your data in exciting and compelling ways, and we made it even easier to execute analytics in the cloud.
The key differences between Amazon Kinesis and Kafka are: Introducing data streamers! These services validate and route messages from one application to another, managing workload and message queues effectively. The result? Users process messages through a centralized processor and handle large data streams more efficiently. Amazon Kinesis and Apache Kafka are two data stream services.
