Modern data and analytics leaders know that every business user is different. No two marketers or finance managers will use data in exactly the same way because no two share the same contextual view or understanding of the business. Their challenges are as nuanced as they are complex. And they need insights tailored to their specific needs if they are to be successful at solving business problems with data. Unfortunately, traditional BI tools treat everyone like carbon copies.

Data transformations are the backbone of smooth-running data operations. Transformations are used in data replication between databases, data migration from cloud to on-premise, and data cleaning (aggregations, outlier removal, deduplication …) aka all the good stuff that goes into extracting insights from data. But as any data professional can attest, transformation can also be a painful bottleneck. Think scripts that run for an entire day and finish just before the next scheduled job.

In traditional data warehouses, specific types of data are stored using a predefined database structure. Due to this “schema on write” approach, prior to all data sources being consolidated into one warehouse, there needs to be a significant transformation effort. From there, data lakes emerge!

Along with its security benefits, an SFTP connection is the quickest and most efficient way to transfer files between two local or remote systems. When transferring files or data from one server to another, using an SFTP connection is one of the best options to ensure this data remains untampered. Utilizing an SFTP connection is especially beneficial for commonly used data integration systems like ETL and Reverse ETL. So what makes SFTP so great, and what is even needed for an SFTP connection?

Buying software is often the answer for busy engineering teams in search of a quick solution with minimum aftercare. But while your team may be sure of the problem, how do you go about searching for a product to fix it? Far from being the 'easy option', there is a lot you need to consider before you invest in a bought solution – user experience, cost comparisons, and support features to name a few. Let’s explore some of the considerations when making a good decision.

Joint customers can now stay within AWS for all cloud services and minimize data movement costs.

Over the past two decades, marketers have faced an uphill battle in trying to turn marketing into a fully data-driven discipline. Our challenge is not that we don’t have enough data but that data has been difficult to access and use. Marketing, sales, and product data is scattered across different systems, and we can’t get a complete picture of what is going on in our businesses.

For our latest specialist interview in our series speaking to technology leaders from around the world, we’ve welcomed James Kaplan CEO and Co-Founder of MeetKai. He founded the startup with his Co-Founder and Chairwoman, Weili Dai, after becoming frustrated with the limitations of current automated assistants. Kaplan has had a true passion for AI and coding since he was six. He wrote his first bot at only nine years old and wrote the first original Pokemon Go bot.

Suppose that you work for the infosec department of a government agency in charge of tax collection. You recently noticed that some tax fraud incident records went missing from a certain Apache Kafka topic. You panic. It is a common requirement for business applications to maintain some form of audit log, i.e. a persistent trail of all the changes to the application’s data. But for Kafka in particular, this can prove challenging.

With hackers now working overtime to expose business data or implant ransomware processes, data security is largely IT managers’ top priority. And if data security tops IT concerns, data governance should be their second priority. Not only is it critical to protect data, but data governance is also the foundation for data-driven businesses and maximizing value from data analytics. Requirements, however, have changed significantly in recent years.