Your data can quickly get out of control when you’re working with multiple cloud storage services and applications throughout your organization. Complex cloud ecosystems can make it difficult to know what data you have, how it’s being managed, whether it’s safe, and how to use it effectively. Cloud data management platforms can stop this frustrating scenario in its tracks.
There are lessons to be learned from the brick and mortar or pure-play digital retailers that have been successful in the Covid-19 chaos. As the pandemic’s stress test of e-commerce, in-store insights, supply chain visibility, and fulfillment capabilities have revealed shortcomings, and long-lasting consumer experiences— it has also allowed many companies to pivot to very successful strategies built on enterprise data and the digitization efforts that accompany it.
Apache Hadoop Distributed File System (HDFS) is the most popular file system in the big data world. The Apache Hadoop File System interface has provided integration to many other popular storage systems like Apache Ozone, S3, Azure Data Lake Storage etc. Some HDFS users want to extend the HDFS Namenode capacity by configuring Federation of Namenodes. Other users prefer other alternative file systems like Apache Ozone or S3 due to their scaling benefit.
Augmented analytics uses emerging technologies like automation, artificial intelligence (AI), machine learning (ML) and natural language generation (NLG) to automate data manipulation, monitoring and analysis tasks and enhance data literacy. In our previous blog, we covered what augmented analytics actually is and what it really means for modern business intelligence.
A data pipeline is a series of actions that combine data from multiple sources for analysis or visualization. In today’s business landscape, making smarter decisions faster is a critical competitive advantage. Companies desire their employees to make data-driven decisions, but harnessing timely insights from your company’s data can seem like a headache-inducing challenge.
Thinking of building out an ETL process or refining your current one? Read more to learn about how ETL tools give you time to focus on building data models. ETL stands for extract-transform-load, and is commonly used when referring to the process of data integration. Extract refers to pulling data from a particular data source. Transforms are used to make that data into a processable format. Load is the final step to drop the data into the designated target.
The execution order of the elements in an Apache JMeter performance test plan is very important, it helps understand the timeline of events and how your test will behave. Make sure that you understand every JMeter element before adding it to your test plan, this will help create faster performance tests and not jump into various errors.
In the last year, I've spent many hours writing patches for libuv and Node to collect new metrics. The goal of this was to indirectly infer the state of the application without introducing measurable overhead. I've run a few hundred hours of benchmarks and collected over one million data points to make sure my analysis was correct. Eventually, I plan to write about all aspects of my research, but today we will focus on a metric that has already been added to Node.
We’re pleased to announce that we’ve recently launched our improved audit log feature and have rolled this out to users across all of Logit.io’s operating regions. The audit log is visible on both account and individual Stack level.
Website load testing services determine how websites deal with online traffic. Too often, people equate it with performance testing. However, load testing is just a type of website performance testing. Other types of performance testing include endurance testing, volume testing, scalability testing, spike testing, and stress testing. In website deployment, it is common that transactions may fail and systems crash. It is due to concurrent demands on the website and adjustment over resources.
