Fast, budget-friendly Big Data solutions

How To Achieve Fast And Responsive Big Data Storages Without Investing An Entire Fortune

Being a competition between leading IT services companies, Big Data market promises to grow only bigger. It should make choosing Big Data storage for your company easy, right? Wrong. When it comes to choosing fast and responsive, yet low-cost storage, typically “pick any two” principle still applies. Is it possible to achieve both high performance and flexibility without going overboard cost-wise?

It is, and here are a few tips that will help in adjusting Big Data storage to your requirements.

Choosing open source big data tools

Big Data storage capacity is important – obviously the more the better. But it’s not the only condition of making it fast and responsive.

Get a proper toolkit of Big Data processing applications, and you will drastically improve input/output performance of a storage. Luckily, quite a few of them are open source. Besides, a wide support base of the developers improves their quality regularly to keep it up to date.

Hadoop and Spark are considered the leading open source frameworks for storing large sets of data. Hadoop, for instance, achieves high data retrieving speed by horizontal scaling. It allows to proceed data way more quickly comparing to SQL based platforms.

Moreover, you get a possibility to optimize server capacity usage. By buying a more capacious server with faster discs you get a respective performance gain with Hadoop. Whereas increasing the amount of the machines to store your data under SQL based platforms can become ridiculously wasteful. At some point, the more powerful physical storage you need, the less productivity outcome you get. In other wordsthe more you pay, the less you get.

A newer Big Data Analytics framework, Apache Spark, is also widely known for its fast performance. It also provides an easy access to your data due to implementation of Dataframes API. It supports different storage formats and has plenty of advanced tools to tweak the usage of your storage resources. You will also find over a hundred of third-party libraries and features for Spark, that were developed by its support community.

Among the other handy open source analytical tools and frameworks are Apache Storm, Apex, SlamData, Drill, HBase, NiFi. As well as a wide variety of cloud services that will stand in good stead for smaller scale companies and startups.

Improving flexibility and speed with NoSQL based technologies

There is a lot of reasons why we love relational data model based storages. But velocity is certainly not one of them. Executing each of the queries takes a bit of time that may seem to be minuscule (insignificant) on a smaller scale. But when you operate hundreds of interrelated tables, it adds up to an unpleasantly impressive speed drop.

Think of trying to get in touch with a certain Empire State Building employee by checking out every single room (even after you’ve found him in the first one) instead of calling him through the reception.

Now, NoSQL based technologies don’t use queries, which fastens data proceeding. Given that, bringing NoSQL distributed databases in your business can make a huge difference. Especially in cases where its efficiency relies on near real-time manipulating large amounts of data. They are reported of being able to speed up data access and management in as much as a hundred times!

Moreover, NoSQL is adjusted to operating unstructured data or data with unannounced structure. It’s a great solution for increasing database’s flexibility. New data types integration will become way more easy if compared to SQL based platforms.

Software-defined storage

This approach allows you to run storage services on general-purpose hardware, making it way more cheap. Your company can choose storage hardware components that work the best for its purposes, e.g. flash storage to increase performance, or more capacious HDDs.

Also, some software-defined storage services let you leverage hardware, which provides one more way of money saving.

In-memory data storage

While not being the cheapest solution, in-memory storage technologies are certainly worth consideration. Comparing to hard disk storage, the improvement of data access speed is reported to be ten thousand to one million faster. It’s mind-blowing!

Due to query optimization, in-memory storage allows to decrease server load, thus to increase its useful capacity. It allows to proceed multiple simultaneous data requests, which is of particular benefit in interactive media business, warehouse management etc. It’s also being a reasonable investment in case your business relies on rapid data changes and fast query response.

Some of the most known platforms providing in-memory Big Data storage technologies are Alluxio and BlueData.

Separation of Big Data and Fast Data

The middle ground business solution between HDD and a RAM data storage is to combine both. It’s not uncommon for the IT companies to use in-memory storage for a part of data that requires quick access and rapid updating, while keeping the main, less frequently changing databases on storage servers. By this approach they achieve a compromise in terms of cost and speed.

As you see, there is quite a few ways to achieve fast and flexible storage for your company Big Data without paying arm and leg. Which one suits you the best?

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    'How To Achieve Fast And Responsive Big Data Storages Without Investing An Entire Fortune' have 1 comment

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      July 15, 2016 @ 1:22 am Chris Dima

      When you put SlamData on top of MongoDB you get a data warehouse that takes a day to stand up. Ultra flexible and open source. And you can use SQL.

      Modern BI for Modern Data.

      Next stop: Spark.

      Reply


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