2022 will be a watershed year for big data, AI and analytics, with more companies expecting tangible business results. But from IT’s vantage point, there is still much work to be done. Here are 10 New Year’s big data resolutions for IT. 1. Establish a data retention policy Many organizations have just kicked the can down the field, avoiding the big data retention discussion altogether.
The database/sql package in the Go standard library maintains a pool of connections so that all queries going through a single *sql.DB instance will reuse the same pool. This is great because you get a connection pool out of the box. But what if you need to share the same connection pool across processes? How do you use the same API in different processes but still reuse the same pool?
Ever since the pandemic hit the world in 2020, many businesses and institutions have begun to adopt cloud computing into their operations. With more employees working from home, companies have moved from on-site data centers and chosen cloud computing services. Although cloud computing is not new, its relevance has increased during the last year and has revolutionized how businesses and institutions are operating now.
Rancher Desktop has been in development for just over a year with the open question: when do we have a 1.0.0 stable release? Along the way the scope has expanded, it was ported to run in more places and the development team has grown. All of this happened as we worked out if Rancher Desktop would be useful for people, what features people want to use and what are good ways to build it. We are finally ready to answer that 1.0.0 question.
Log4j, the popular open-source logging library, had a rough December and closed out the year with an impressive streak of four critical vulnerabilities so far. Many are calling this the worst cybersecurity event in history. Again, so far.
The state of backup technology has moved forward, and many of the old assumptions are no longer true. For years, image-based backup was considered the industry standard. But today’s IT professionals really need full-system recovery capabilities without the built-in inefficiencies of legacy image backup products. Image-based backup backups are far from ideal, especially for managed services providers (MSPs).
When your Elasticsearch cluster ingests data, it needs to understand how the data is constructed. To do this, your Elasticsearch cluster undergoes a process called mapping. Mapping involves defining the type for each field in a given document. For example, a number or a string of text. But how do you know the health of the mapping process? And why do you need to monitor it? This is where mapping statistics come in. Mapping statistics give you an overall view of the mapping process.
