6 Tools that will help you with big data

Big data is both a challenge and an opportunity for modern businesses, and you can only extract optimal value from the information you accumulate with the help of specialist tools.

Knowing which tools to acquire and put to work in the first place can be an uphill struggle, so let’s take you through a few of the top options that you should prioritize in your own big data strategy.

A brief word on big data

Big data can be described and explained in lots of ways, and used to refer to a raft of tech and tactics. The overarching idea is that businesses can collect info from various sources very quickly, and so need software to store, sort, interpret and analyze it.

These tools need to go above and beyond traditional analytic solutions, since the data being handled can be unstructured and overwhelming. And where multiple sources are involved, the complexity can increase quickly.

SSIS

SQL Server Integration Services, otherwise known as SSIS, is a tool used for extracting data from a database. This of course relates specifically to databases which use SQL, or structured query language.

SSIS varies depending on the SQL solution you are using, and most commonly it crops up in the context of Microsoft’s SQL Server.

As you might already know, SQL is best at handling structured data, so it can form part of a broader big data implementation without taking on every type of duty. With the help of SSIS, information from an SQL database can be extracted and moved elsewhere, making it easier to integrate in whatever way you see fit.

MongoDB

Contrasting with SQL Server, MongoDB is based around the NoSQL standard. While initially this management style was made entirely without SQL, support has since been added, hence the reason that it now refers to ‘not only SQL’ rather than being entirely exclusionary of structured query language.

In the case of MongoDB, the main aims were for scalability and support for larger loads of non-tabular data, rather than the table-based approach of SQL.

Developers see MongoDB as straightforward to get to grips with, and are also attracted by its open-source nature. It is implemented by all sorts of well-known tech titans, including Google and eBay to name but two.

Xplenty

Following a similar route to SSIS, the role of Xplenty is to pull together data and process it so that it can be analyzed in cloud-powered big data environments.

It is cloud-based in its own right, and thus provides ample flexibility for organizations that may not have the in-house resources to manage resource-intensive functions like these.

Another of its benefits is that it lets you put together custom data pipelines without even needing to code them yourself, although there is more user control available for those with the coding chops.

So long as you are willing to pay annually, Xplenty can help to avoid data source fragmentation and fuel your big data analytics efforts effectively.

Tableau

So you’ve got a handle on your data and you’ve carried out comprehensive analysis to glean insights from it. Next, you need to be able to convey this in a way that other human users can understand, even if they are not necessarily data scientists in their own right.

Enter Tableau, a visualization tool that helps all sorts of teams to divine meaning and drive decision-making from graphically represented data sets.

Again, it is not necessary to be a programming stalwart to make the most of this software, although there is still a learning curve to overcome if you are completely new to Tableau’s interface and abilities.

Integration with different data sources is a comparative breeze, and the only caveat is the cost, which fans of the tool will consider worth paying.

Zoho Analytics

If you are an up and coming business that does not have a lot of money or resources to throw at the problem of big data, then working with a platform like Zoho Analytics could let you dip your toe in the water without leaving you flailing.

As you probably realize, this is a tool which sits within the wider Zoho framework of solutions, so if you are already taking advantage of the customer relationship management and HR packages from this brand, embracing the Analytics product as well will be the most seamless option.

Once in place, this tool empowers you with a dashboard-based, comprehensively visualized overview of the data that is pooled from the different components of the Zoho sphere. This can be useful for everything from sales and marketing to customer support optimization.

The somewhat basic nature of this setup, and its imperfect scalability, means that small and medium sized firms will be better suited by Zoho Analytics than their larger counterparts.

Apache Hadoop

With a large following among major corporations and a whole host of big data uses to its name, Hadoop is favored by everyone from AWS to Intel.

One of its key components is the file system which can cope with most major data types, including multimedia files as well as text documents and more besides.

Written in Java, Hadoop is an all-encompassing and thus resource-hungry solution. So while it may not be the fastest performer, it is perfect for organizations that are willing to invest heavily in the hardware required to keep it running smoothly.

Another aspect of Hadoop’s ecosystem which makes it appealing is its high availability design. While other tools might be prone to the potential for unplanned downtime, the extensive redundancy features of this platform allow it to underpin big data solutions that need to be exceptionally resilient.

The last word on big data tools

Hopefully you now recognize that there are tools to assist you with reaching your big data goals regardless of the size of your business.

From startups to established brands, big data struggles can be overcome through a combination of the right software and hardware. It is simply a case of choosing wisely.

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