Apache Spark Streaming vs. Dataminr Pulse

Apache Spark Streaming is a tool that we are using for almost a year and is excellent in managing batch processing. It is user-friendly. Using it, we can even process our massive data in fractions of seconds. Its pricing is its other plus point. Only its In-memory processing is its demerit as it occupies a large memory.

Incentivized

For any news editor, Dataminr is a must. It is the only platform I have used which I would 100 percent recommend to anyone who needs constant access and notification to all that is going on in the world's media. Various other platforms provide information, but Dataminr collates all these other platforms together into one easy to use interface. It is remarkably quick to check Dataminr after receiving a desktop or email notification and find out what is going on regarding a specific subject. As a news editor, I am required to manage a news team that produces copy for breaking stories, and Dataminr allows me to find news and commission it faster than any other platform I have previously used.

Incentivized

• It is amazing in solving complicated transformative logic.
• It is straightforward to program.
• It is a very quick tool.
• It processes large data within a fraction of seconds.

Incentivized

• Collating various sources into one interface makes keeping track of breaking news remarkably easy.
• The live update feature means updates from breaking stories are fed directly to the consumer. This means users do not have to go out their way to find out every last detail of a story, and can instead use their time writing the story.

Incentivized

• There must be more documentation.
• It is a profoundly complex tool.
• Its in-memory processing consumes massive memory.

Incentivized

• Setting up Dataminr can be relatively obtuse. Although basic functions are easy to use, the more in-depth aspects require research and training to use correctly.
• Topics are very broad, which can be a negative at times. It would be useful to have 'football' as a topic rather than 'sport', for instance.

Incentivized

Connection issues/downtime occur but they are rare and generally quickly fixed. There are frequent seminars and tutorials to teach the platform. This is an excellent decision from the company and has made me more willing to learn how to use Dataminr correctly. As a result of this, I use the platform more than I would have if I had been forced to learn everything myself.

Incentivized

Apache Spark Streaming stands above all the huge data transformative tools because of its speed of processing which was quite slow in Presto as it takes a lot of our time in the data processing. Spark, comfortably provides integration with Jupyter like notebook environment. and Spark's combination with Jupyter and Python results in enhancing the speed .

Incentivized

Before using Dataminr we did not use a similar platform, with stories discovered by simply crawling the web (TweetDeck/Reddit/news sites, etc). Because of this, Dataminr has been a game-changer for us and has revolutionized the way we have worked since it was introduced. Being able to completely remove one aspect of the job (searching for news) has increased productivity in all other areas.

Incentivized

• Cost and time-effective tool for our business.
• We can integrate with Jupyter with many conveniences.
• Its high-speed data processing has proved beneficial for us.

Incentivized

• Since starting to use Dataminr, our traffic has vastly increased. Though not entirely to thank for the growth, Dataminr has played a role in talkSPORT.com's ComScore increasing by 150 percent over the past 18 months.
• Increased traffic is partly due to producing more content, and Dataminr has directly led to discovering a number of stories we previously would have missed.

Incentivized