Related Content
 |
3 Serverless Strategies to Look for in 2021 In this article, we examine the three serverless applications deployment and development approaches that are transforming the application development process and acting as a catalyst for fast adoption of the DevOps practice across the board. |
|
 |
What Exactly Is Serverless? The word serverless—it’s everywhere. The word has been Googled an average of 100 times daily in 2020. Is serverless just a buzzword? A facade? Or a world where we won’t need servers anymore? |
|
 |
Requirements Discipline: Avoiding “Death by a Thousand Paper Cuts" Absent an effective requirements baseline it is difficult to distinguish clarifications and error correction from enhancements and changes to the original ask. |
|
 |
Breaking Down Apache’s Hadoop Distributed File System Apache Hadoop is a framework for big data. One of its main components is HDFS, Hadoop Distributed File System, which stores that data. You might expect that a storage framework that holds large quantities of data requires state-of-the-art infrastructure for a file system that does not fail, but quite the contrary is true. |
|
 |
Comparing Apache Hadoop Data Storage Formats Apache Hadoop can store data in several supported file formats. To decide which one you should use, analyze their properties and the type of data you want to store. Let's look at query time, data serialization, whether the file format is splittable, and whether it supports compression, then review some common use cases. |
|
 |
5 Pitfalls to Avoid When Developing AI Tools Developing a tool that runs on artificial intelligence is mostly about training a machine with data. But you can’t just feed it information and expect AI to wave a magic wand and produce results. The type of data sets you use and how you use them to train the tool are important. Here are five pitfalls to be wary of. |
|
 |
Benefits of Using Columnar Storage in Relational Database Management Systems Relational database management systems (RDBMS) store data in rows and columns. Most relational databases store data row-wise by default, but a few RDBMS provide the option to store data column-wise, which is a useful feature. Let’s look at the benefits of being able to use columnar storage for data and when you'd want to. |
|
 |
Comparing Apache Sqoop, Flume, and Kafka Apache Sqoop, Flume, and Kafka are tools used in data science. All three are open source, distributed platforms designed to move data and operate on unstructured data. Each also supports big data in the scale of petabytes and exabytes, and all are written in Java. But there are some differences between these platforms. |
