Which of the following sources is likely to produce big data the fastest? The answer, in short, is Google. Google’s indexing, sorting, and indexing of information it acquires from its various search components quickly and reliably produces quick insights into the nooks and corners of any given topic. The speed with which Google’s information-aggregating index changes and grows is precisely what makes it a “source” of Big Data. In addition, Google is renowned for being fast to make changes in its data warehouse and is always looking for new ways to collect, sort, and process its massive pool of information.
What’s more, Google has been making data-processing progress for several years now. In 2021 it launched the first of its type of mobile computing device, the Android. The next year it released the smartphone-oriented Android Kit Kat, and a year later it introduced its 2.0 smartphone operating system. Today, nearly all Android devices are equipped with Google’s in-memory analytics service, which is responsible for collecting and processing user data in a manner that’s highly targeted and constantly updated.
However, one of the most recent technologies to arrive on the scene is a program called Spark that aims to take advantage of Google’s huge data infrastructure. Rather than attempting to learn from Google’s big data warehouse, Spark aims to create its own customized solution for enterprise app development. What makes Spark so different is that it leverages the low-overhead benefits of a large data warehouse in order to drive up the speed with which information is processed. In fact, this is precisely what web analytics experts like Andy Conrad have been advocating for years.
So now let’s turn to the second question: which of the following sources is likely to produce big data the fastest? The answer is streaming analytics. As the name suggests, streaming analytics deals with the gathering of real-time data as it happens. The problem with traditional waterfall or semi-annual reporting systems is that they don’t reflect real-time events; instead we see a series of averages over time.
This is why many of the large data elements in such systems – Hadoop, for example – were designed to deal with short bursts in input and output. For example, MapD’s very fast collection and consumption of key performance indicators (KPIs) can be seen as acting in real time. But in doing so, it renders the traditional waterfall method of monitoring slower. So which of the two sources is likely to produce big data the fastest, in-memory analytics or grid computing? The answer is streaming analytics.
As a matter of fact, it’s true. Streaming analytics is often used in conjunction with mapreduce and other data scientists. MapReduce is a framework which operates on many large data sets, in order to achieve much higher throughput than is possible with more basic analytics systems. As such, when used in combination with streaming, mapreduce can achieve the throughput of about ten petabytes per second.
In addition, using mapreduce, it’s very easy to saturate an application. For example, let’s say we have a web site that serves thousands of individual pages. If we use a traditional system, say Hotlist, and have two nodes. One of which is a cold standby and the other a hotshot node, which is a secondary node which is linked to the primary, but not connected to the standby, then we’ll end up with a slight delay between requests for pages, because the hotshot node will have to process the request from the cold standby.
If we instead choose to use streaming and mapreduce together, then the overall throughput can be much higher, because there are two parallel nodes. What’s more, it’s possible to have queries that are true, false diff events, or where the actual key is not available in the secondary data set, because the hotplugged replica of the key will always contain the true value. This ensures the system always responds to the real key, and therefore is able to respond at much higher speeds than would be possible with a traditional database. In fact, this solution is so fast that your queries will respond at the rate of light during peak hours, and at the speed of light even during quiet times!