Data Management explained
Data is the information that drives business. It can be structured in rows and columns, like a customer name, address, and phone; and it can be unstructured, such as an email or a social media post. Structured data is what is populated in Relational Database Management Systems such as those created by Oracle, IBM and Microsoft, and open-source PostgreSQL and MySQL, among others. That data can be accessed using the standard Structured Query Language (SQL). Unstructured data resides in what are called NoSQL databases, such as Cassandra, Couchbase, MongoDB and many, many others. Many organizations today run both kinds of databases.
Once the data is stored, it must be easily retrievable, found amid the mountains of data organizations collect, and made available at scale. Numerous tools exist for those jobs, including Hadoop, Apache Spark and many more. It is through the collection and analysis of data that businesses can make decisions that affect their bottom line.
Academic data is widely available, yet widely underused. Why?
Academic data is one of the most underutilized forms of data that exist today. Outside of educational institutions, the only significant use of this data is in enrollment verification, though even that use case is not particularly widespread. And yet, academic data holds massive potential in providing predictive behavioral signals that could directly benefit the … continue reading
Switching to NoSQL is easier than you might think
While many companies still utilize relational databases, the benefits of NoSQL databases are clear, whether that’s the ability to handle large volumes of structured or unstructured data, perform Agile sprints or its flexibility and scalability. And moving to a NoSQL database is easier than most think. Brian Hess, the strategic solution engineer at DataStax, explained … continue reading
Industry watch: The little dirty data secret
Our industry has a dirty little secret. Come closer, I’ll whisper it to you. (Much of the data held in organizational databases, warehouses, lakes and stores is not very good.) There, I’ve said it. Data quality remains a persistent problem for enterprises, and there are many reasons as to why. It could be that fields … continue reading
ArangoDB boosts multi-model database performance with release of ArangoDB 3.6
ArangoDB, the leading open source native multi-model database, today announced the GA release of ArangoDB 3.6. ArangoDB 3.6 introduces OneShard, the ability to restrict individual databases to one node in a cluster, to ArangoDB’s Enterprise offering, and also includes major performance improvements that increase query speeds up to 30x faster. A database created with OneShard enabled is … continue reading
erwin revamps Data Modeler solution
Data governance solution provider erwin is releasing a new version of its data modeling solution. According to the company, the erwin Data Modeler is designed to help users design, deploy and understand high-quality data sources. The new version features a new UI that makes it easier to customize the modeling canvas and access features and … continue reading
Is there still a role for integration specialists?
According to Gartner Technical Professionals analyst Matt Brasier, the answer is a definite ‘yes.’ He explained: “First of all, integration specialists will still need to do all of the hard bits of integration; the bits at the back end where you don’t have a REST API exposed by the system because it’s a 15-year-old ERP … continue reading
Data analysis: Batch or event-driven?
Organizations are looking for collect and analyze data faster, and in more real-time as well. So, does moving to an event-driven approach to data improve on batch processing? Gartner analyst Matt Braiser said, “Rather than having your sales figures move in a batch overnight and then having a reporting tool that runs on that and … continue reading
2020: The year of integration
Software development has changed, moving from monolithic code blocks to a cobbling of open source and services. Delivery has changed, as organizations moved from on-premises servers to the cloud, and end points such as smartphones and all manner of IoT devices have become ubiquitous. How data is distributed and consumed has changed, as containers may … continue reading
Kotlin’s approach to data science
JetBrains is providing new details on the current state of Kotlin tools and libraries for data science. “How does Kotlin fit data science? Following the need to analyze large amounts of data, the last few years has brought a true renaissance to the data science discipline. All this renaissance of data science couldn’t be possible … continue reading
Facebook’s tumultuous times: new OS in the works amid another breach
In a year where data breaches at Facebook have seen no signs of slowing down, another blow has been dealt. A reported 267 million Facebook user IDs and phone numbers were exposed online for anyone to access without a password or any authentication. Most of the records affected users from the United States. The UK … continue reading
Software predictions for 2020 from around the industry
Thought leaders weigh in on what we can expect from the software development industry in 2020: Adam Scroggin, CEO of CardBoard DevOps will continue to be key as we move toward 2020. Software teams will notice more and more that once a product is released, it is not done. Software products are never done. We … continue reading
SD Times news digest: JDK 14 Rampdown Phase One, Codota acquires code prediction tool TabNine, and Apache Log4J2 2.13.0
The overall feature set for JDK 14 is now frozen and includes pattern matching for instanceof, a packaging tool and NUMA-Aware Memory Allocation for G1. The developers behind JDK 14 said the the stabilization repository, jdk/jdk14, is open for select bug fixes and, with approval, late enhancements per the JDK Release Process (JEP 3). The … continue reading
