When it comes down to selecting a big data management platform, not all software is equal. Some feature more speed at the cost of flexibility while others are too expensive despite the amount of utility they offer. Here are some key factors you need to put into consideration.
In defining Big Data, ‘the 3Vs†– volume, variability and velocity – are the most common parameters used to encompass how the data gets classified. The main goal of having a data management system within an organization is to maintain high levels of data quality. Following this, the data should be easy accessibility across different departments or teams. It can then find a use for analytics and other business applications.
Big data management involves concepts that relate to the collection, administration, storage and processing of large amounts of structured and unstructured data. This also includes the policies and procedures used to facilitate the same.
Managing such large volumes of data is nothing if not involving and complicated, especially so without a well-polished workflow to ensure data integrity. It’s made even more difficult if the business lacks a platform it can use to abstract away the inherently complicated nature of dealing with big data.
The need for a data management system
Most businesses that handle such vast amounts of data normally don’t have the luxury of having it formatted uniformly. When collecting data for natural language processing (NLP), for example, you might get data from books (epub, pdf or mobi formats), websites (HTML), social media sites (JSON) and scraping data from video files. All of these need to have high levels of management.
Updating, cleaning and storage are all necessary for as you need it for easier retrieval for processing in future. This has a direct impact on the overall organizational efficiency, so it’s of great importance.
Data management is also important because it saves businesses from a lot of financial and legal liabilities. According to the National Archives & Records Administration, 93% of companies that had lost their data centers for 10 or more days files for bankruptcy within a year. At the same time, 50% of businessesOpens a new window without a proper data management system went bankrupt almost immediately, directly related to the downtime.
Lastly, the legal implications of not having a data management system come about in the event of a data breach. The post-2010 era was riddled with controversy after controversy due to data leaks and breaches from big companies such as Facebook, Google and Equifax. Most of these breaches are traceable to improper data management along the way.
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What to consider when choosing a big data management application
When it comes down to it, not all software is equal. Some feature more speed at the cost of flexibility while others are too expensive despite the amount of utility they offer. When selecting a big data management platform, here are some key factors you need to put into consideration.
Where the data hosted: on-premises vs. the cloud
The first major consideration you have to contend with is whether you want to host the data on your own servers or cloud. For most companies, the obvious solution would be to pick the cloud because of easier scalability and low maintenance costs.
Indeed, cloud-based services are growing at a faster rate than ever, as more companies abandon on-premise offerings for more sustainable cloud infrastructure. According to Gartner, the cloud services industry will grow to about $214 millionOpens a new window by the end of 2019, up 18% from the previous year. The fastest-growing segment in all of this is the infrastructure as a service (IaaS) market.
While the cloud might be a sufficient offering for most companies, it’s far from a comprehensive solution. Organizations with legal requirements such as locally hosting data or strict security requirements need to host their data on premises. For companies that have already made significant investments in on-premises solutions, switching to cloud-based infrastructure might also be expensive. Such companies would rather continue as-is or opt for a hybrid approach at the data management.
How much control do you need? Open source vs. proprietary
Some of the most popular big data management systems in the market, such as Hadoop and SparkOpens a new window are open source. These have very permissive licenses and allow you to do pretty much anything you want with them. They allow for more control, more easily-defined terms of ownership and fine-tuning to optimize for performance.
The downside of open-source software is that it needs a lot of professional expertise. Setting up, managing and maintaining a single Hadoop cluster is too much work for a single individual. Not to mention the fact that these need constant supervision to see if every cog in the machinework is moving effectively.
To mitigate that, there also exist managed open-source systems. These abstract away the need for special expertise and effort in configuring and managing big data software. On the other hand, they tend to be expensive, especially in the case of those that demand monthly payments rather than pay-as-you-go subscriptions.
Enterprise software, also known as closed-source software, caters for the needs of enterprises that don’t have the need or capacity to handle open-source software. These come with the advantage of the support and consulting services, and may even be faster at crunching data than open-source solutions. However, licensing or subscription costs may be a major hurdle.
How much do speed do you need? Batch processing vs. streaming
Early big-data solutions, particularly Hadoop, introduced the concept of batch processing to the big data world. This occurs when data processing happens to already-stored data in chunks.
For instance, crunching through a company’s financial records to determine their credit-worthiness. This will happen via batch processing if the company is so inclined. If the company in question is particularly prolific spender, the number of transactions will be in the millions, having accumulated over the years.
Batch processing will take quite a while to process, which is its biggest downside. However, it produces more detailed insights and handles large volumes of data quite easily.
A simpler way to think of stream processing is as ‘live processingOpens a new window .’ As the name suggests, it works best when the company needs analytics data in real-time (or, more correctly, in a very short period of time.) Multiple applications cropped up to fill the void left open by Hadoop such as Samza, Spark, Flink, WSO2 and Storm.
Stream processing works best for applications such as fraud detection, where the data should be delivered back right before a transaction. Despite its speed, look out for how hungry it is with resources. Spark, for example, manages to be almost three times faster than Hadoop by utilizing in-memory processing. RAM is comparatively more expensive than disk processing as used by Hadoop.
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What to look for in a big data management system
When you’ve sufficiently narrowed-down the requirements you need from a proper data management system, you’ll likely still have two or three applications on the table for consideration. To help you down the requirements a little further, here are a few criteria that will help you to eliminate certain projects in favor of others.
Performance – Batch processing software such as Hadoop are very performant in their own right. The only thing that sets modern solutions apart from these is the fact that Spark, did instance, is incredibly fast. With regards to performance, your company will have to define its own criteria for what constitutes a good performance.
Scalability – When getting into big data, one of the most important factors you need to consider is scalability. The amount of data produced on the internet is growing larger with every passing year. One should always assume that data will continue to grow indefinitely, hence the need for a scalable application.
Usability – Usability refers to how easy it is for new and existing engineers to adopt new technology. Again, not all software is equal in its creation. Some sacrifices simplicity for the sake of power and others have a small learning curve but are considerably less powerful.
Usability also encompasses a few other crucial factors that relate to big data, such as scalability. Some software are a lot easier to scale than others – some work better in distributed environments, such as Kafka for an event-streaming platformOpens a new window . Others arose out of a need for software to use in vertically-scaled environments.
Tools that are simple to learn, deploy and don’t need constant management from teams can provide tremendous value. The better the default configuration, such as only having certain ports open and using SSL by default are also preferable. They require much less tinkering.
Security – Much of the data included in big data stores is sensitive information that would be highly valuable to competitors, nation-states, or hackers.
Organizations need to ensure that their big data has adequate protection to prevent the sorts of large data breaches that have recently been dominating headlines. That means looking either for tools that have security features like encryption and strong authentication built in or tools that integrate with your existing security solutions.
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