Data streaming: The best use cases and why they work

Could IoT sensors, geo-tracking, customer interaction, and other data streaming services help your bottom line? Learn from these successful uses.

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When your company needs answers from big data in real time, the data is streamed so it can be filtered or sampled, aggregated and correlated—all to bring about a real-time answer to an important business question. “Streaming data is table stakes for the most interesting future use cases,” said Mark Brewer, CEO at Lightbend. “And that’s giving rise to the number of programming languages, frameworks and tools for building and running streaming data-centric applications.” However, streaming data—especially unstructured big data like what comes from Internet of Things (IoT) devices—can require knowledge of sophisticated processing architectures and investments in tools as well.

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This is why real-time data streaming of big data like IoT,

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5 lessons project managers can learn from the first 2020 US presidential debate

While projects aren’t the same as politics, there are similarities. Here are ways to become a better manager by learning from the debates.

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Last week’s 2020 US Presidential Debate offered important lessons that can help project, program, and portfolio managers improve their effectiveness when communicating. Here are five ways you can learn to be a better leader.

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1. Be aware of distractions

Distraction was a key element in the first presidential debate and likely served as a means of confusion for listeners. It blanked the entire debate and made it difficult for the audience and the two candidates. The result was a lost opportunity to share their intended messages and plans for the future with stakeholders.

Distraction can be a strategy of some to reduce the effectiveness of others. Project leaders should

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Time series databases are hot, but will this spin on open source be cool?

Commentary: Timescale has a novel licensing model that it hopes will be “open enough” to create a community. Will it work?

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For decades developers would build applications using the same database tools. They might opt for Oracle over IBM’s DB2, or for an open source database like MySQL or PostgreSQL, but they were nearly always all using a relational database, speaking SQL. 

My, how times have changed.

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Today developers have a smorgasbord of options from which to choose, whether document or key-value or columnar or relational or multi-model. But over the past two years, no database category has seen more growth than time series databases, something evident a year ago but now glaring in its obviousness. When I asked Timescale CEO Ajay Kulkarni why this once niche, now prevalent approach has gained in popularity, he explained it as a matter of data fidelity: “Time

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COVID-19 isn’t slowing open source—watch for developer burnout

Commentary: New data shows that open source development remains resilient in the face of global upheaval due to the coronavirus pandemic, but developers’ work-life balance may be paying the price.

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Though life has resumed a measure of normalcy for many in the wake of COVID-19, earlier in 2020 life came to an abrupt stop. No more travel. Tightly restrained social interaction. Widespread adoption of work-from-home policies. Everything changed.

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Everything, that is, except open source software development. 

Despite the difficulties of WFH arrangements (increased childcare duties, social isolation, unfamiliar work arrangements, etc.), open source software developers didn’t miss a beat. In fact, as evidenced by data from the Apache Software Foundation (ASF), code commits increased in the wake of the global pandemic. As ASF president David Nalley related in an interview during Apacheon 2020, “The asynchronous, distributed communications and decision-making of open

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Snowflake data warehouse platform: A cheat sheet

The Snowflake platform offers all the tools necessary to store, retrieve, analyze, and process data from a single readily accessible and scalable system.

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The digitization of the modern business enterprise has created a seemingly never-ending stream of raw data. Gleaning actionable nuggets of information from terabytes upon terabytes of data requires systems that can store, retrieve, and process data quickly and efficiently. One of the most common of these systems is called a data warehouse.

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Data warehouses are central repositories of integrated data obtained from one or more disparate sources. These systems store current and historical data in one single place, which can then be retrieved for the development of analytical reports and other actionable information for employees throughout an enterprise. A well-designed and comprehensive data warehouse can unleash the power of institutional collected data.

Developing and

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Developers, get ready for the collision of DevOps and data analytics

The COVID-19 pandemic has accelerated the blending of data analytics and DevOps meaning developers, data scientists, and product managers will need to work more closely together than ever before.

The COVID-19 pandemic has accelerated the blending of data analytics and DevOps meaning developers, data scientists, and product managers will need to work more closely together than ever before. In this episode of TechRepublic’s
Dynamic Developer

, host and TechRepublic editor-in-chief Bill Detwiler speaks with Michael O’Connell Ph.D., chief analytics officer at TIBCO Software about how data analytics is merging with DevOps, the data science work his company has done helping organizations respond to the COVID-19 pandemic, and what that works tells them about the future of software development and analytics. The following is a transcript of the interview, edited for readability.

Bill Detwiler (00:16): All right. So before we get started talking about data analytics and DevOps, and how the

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