The meltdowns at Southwest and the FAA, just weeks apart, were because of weaknesses in systems scheduled for upgrades--underscoring the urgent need to give priority to efforts to modernize those systems, as well as the consequences of waiting to do so, the consultants said. While starting over wholesale with new information-technology infrastructure is likely unrealistic, consultants said, the sector should take advantage of cloud-based tools that can integrate the fire hose of real-time data driving airline operations. Newer, cloud-based infrastructure and databases can scale horizontally--meaning they can take advantage of distributed computing resources across the internet as needed. This design allows information to flow more freely, reducing the likelihood of glitches that cascade into systemwide shutdowns. Older, legacy systems are limited to the amount of computing power available.

Databases are fundamental to training all sorts of machine learning and artificial intelligence (AI) models. Over the last two decades, there has been an explosion of datasets available on the market, making it far more challenging to choose the right one for your tasks. At the same time, the larger number of datasets means you can find the perfect fit for whichever application you're aiming towards. Powered by Oracle, MySQL is one of the most popular databases on the market. Created in 1995, it has consistently been one of the top open-source relational database management systems (RDBMS) used by major companies like Facebook, Twitter, Uber, and Youtube. What led to its rise in popularity?

Making predictions for the year ahead is an age-old tradition, but some are easier than others. For instance, saying 2022 will see a new Marvel movie is a pretty safe bet. Looking into a crystal ball and predicting technology movements is a lot harder -- especially with factors like Covid, climate change and ongoing supply chain disruption adding so much chaos to the mix. There is also the risk that overblown promises for one technology or another can wear out readers, making them much less trusting of future predictions. With all of this in mind, here are my predictions for technology in 2022 -- not based on any clairvoyance, but on my understanding of technology, industry and society.

Imagine a world where you own some sort of building whether that's a grocery store, a restaurant, a factory… and you want to know how many people reside in each section of the store, or maybe how long did the average person wait to be seated or how long did it take the average factory worker to complete their assembly task. Currently today these systems are either not using AI and instead use a mix of sensors and buttons to track certain actions or they do use AI but in a way that's highly specific to their use case and hard to easily modify for new use cases that come down the line. This is where BrainFrame comes in. BrainFrame is a tool that connects to all your on-prem cameras and lets you easily leverage AI models and business logic. Alex Thiele is the CTO of Aotu the company that makes BrainFrame and he joins me today to talk about BrainFrame and the vision for a future where computer vision can be run by anyone.

We are looking for a Technical Data Analyst and Program Manager to build out our extended data collection and performance analysis activities. Your job will be to gather and analyze large amounts of raw information from both internal and external sources such as Salesforce, AWS, StackOverflow, Couchbase, GitHub, Google Analytics or custom APIs. You will establish routine reporting and analysis derived from that data, evaluating the trends of our KPI's such that we remain informed as we evolve our objectives. We will rely on you to extract valuable business insights from this work as well as lead cross-functional projects and discussions as program manager for teams that are influenced by this information. In this role, you should be highly analytical with a background in analysis, math and statistics.

It was a memorable year, to be sure, with plenty of drama and unexpected happenings in terms of the technology, the players, and the application of big data and data science. As we gear up for 2018, we think it's worth taking some time to ponder about what happened in 2017 and put things in some kind of order. Here are 10 of the biggest takeaways for the big data year that was 2017. Teradata, for instance, found that 80% of enterprises are already investing in AI, which backed similar findings from IDC. Nevertheless, the same old challenges that kept big data off Easy Street also emerged to cool some of the heat emanating from AI. Over the summer, Databricks' CEO, Ali Ghodsi, warned about "AI's 1% problem."