5 Best Data Processing Software: Complete Guide

Data Analysis
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Data is collected in so many ways within businesses. Typically, the more data you have, the better, but the caveat here is that’s only true if you know what to do with it. Making data usable is the sought-after secret, and with data processing software, it's possible to transform raw data into business insights without the need for human interference or effort.

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In this article, we’re going to share what data processing software is, the critical importance of data processing, the types of data processing, and what this all means to your business’ success. We’ll reveal all the “secrets.”

Coming Up

1. What is Data Processing Software?

2. What are the Best Big Data Processing Software Tools?

3. What are the Stages of Data Processing?

4. What are Data Output File Types?

5. What is the Importance of Data Processing?

6. What are Common Features of Data Processing Software Tools?

7. What are Alternatives to Big Data Processing Software?

8. What are the Types of Data Processing?

9. Wrap Up

What is Data Processing Software?

So, you’re wondering what data processing software is and how it can be useful for your business? Chances are, you’re trying to sort raw financial data to make it useful or wanting to make effective business decisions with data input.

Either way, software for data processing ensures that your data is sorted through efficiently and shifted into a user-friendly format so it can be used for business critical decision-making.

What are the Best Big Data Processing Software Tools?

Let’s talk about some of the best data processing software tools on the market:

1. Google BigQuery

One of the best data processing software is Google Big Query. Google BigQuery is a highly scalable data warehouse which is serverless and comes with a built in query engine.

Since there is no server involved with Google BigQuery, it makes it easier for users to analyse any data and share insights via datasets, queries, spreadsheets and reports.

2. Amazon Web Services

Amazon Web Services has a data processing software called Amazon Redshift, which is a data warehouse that analyses data using existing analytics softwares.

Amazon Redshift’s data warehouse architecture lets users automate basic administrative tasks, and data backups are also automated.

3. Hortonworks

Hortonworks DataFlow (HDF) is an open source framework which can help process large amounts of data. HDF manages taking the data and transferring it to Hortonworks Data Platform. It can also see all the various amounts of data types.

4. Cloudera

Cloudera is a platform which provides more flexibility for hybrid data management. Using the Cloudera Data Platform (CDP), you’d have more efficient data management processes in the cloud.

While Cloudera has a data storage and processing platform based on Apache Hadoop, it also has its own data management system for deployment and operations.

Our Recommended Pick: SolveXia

While SolveXia isn’t solely meant to be a data processing software, it can complement your solutions and help you to accomplish more in less time.

SolveXia is a cost-effective, robust, and no-code automation platform that can maximise your organisation’s efficiency and help you to do more with the data available.

SolveXia connects your business’ data with APIs and integrations so that all data is made usable, even that from legacy systems. With a centralised repository for data, SolveXia is also able to cleanse and transform data into a unified format so it can reach its potential.

With the automation solution implemented, you can streamline account reconciliation, expense analytics, regulatory reporting, rebate management, and additional crucial finance functions.

Along with its data processing capabilities, SolveXia can be used for process automation, to generate and streamline reports, and provide its users with easy-to-read visualisations and customisable dashboards.

What are the Stages of Data Processing?

There are many stages that take place in data processing. Data processing takes place in a series of sequential steps that follow this flow:

  1. Data collection: Collecting raw data from accurate sources is very important so that the findings will be valuable and useful. Examples of raw data are monetary figures, profit/loss statements, user behaviour, etc.
  1. Data preparation: This stage basically entails sorting through and filtering the raw data to remove unneeded or inaccurate data. This step is also there to ensure the data is in a good format for further analysis and processing.
  2. Data input: Raw data is converted into a machine readable format and fed into a processing unit.
  3. Processing: Raw data is subjected to a variety of data processing methods using machine learning and other algorithms to get the desired output.
  1. Data output/interpretation: The data is finally sorted through and processed so it can be displayed in easy-to-read formats for users, such as graphs, tables, audio, video, etc.
  2. Data storage: Storage is where data and metadata can be stored for the future. Having this storage available allows for quick access when needed, and it can also be used to further data processing.

What are Data Output File Types?

Once your data processing software has sorted through raw data, processed it and made it into a format that is palatable for you, the next step is data output.There are several different data output file types that will help you better understand and be able to present your data in a usable way:

  • Text: Used to tell a story for data.
  • Chart: Used to show trends in data such as growth or decline.
  • Table: This is used to present mainly statistical data using rows and columns.
  • Image: Maps, vectors and other images can be used here to demonstrate particular insights from the data retrieved.

What is the Importance of Data Processing?

Data processing is important for most business functions and decision-making abilities. Since collecting data has been made easier with the aid of technology, it means that there’s a massive influx of data volume in businesses of all sizes.

Without proper data processing, that data sits there without providing its value. Rather, it’s costly to store and presents a risk when proper security measures are lacking.

With data processing capabilities, the data is able to provide the crucial value that it is there to serve. Data processing helps to collect, manage, store, and transform data to glean insights from it. This way, businesses can better serve their customers, employees, and stakeholders, thereby enabling the ability to make sound decisions.

When working in finance, neither data nor its applications are new to you. Your day-to-day and repetitive responsibilities like account reconciliation, expense management, and reporting rely on access to accurate data.

Data processing software and complementary solutions like SolveXia can connect data, streamline functions, and transform data into insights.

What are Common Features of Data Processing Software Tools?

There are many features which make data processing tools effective for big data processing. Below are the common features you’ll find among data processing software:

  • Easily embedded results for reporting: Data insights should be easy enough to read and understand in order to make business critical decisions in real time.
  • Raw data processing: Organises data to make it meaningful.
  • Security: When it comes down to big data technologies, it’s essential to have security features which are data encrypted and protect your organisation’s information.
  • Serverless: You no longer need to worry about upgrading, securing and managing business data so you can focus more on deriving conclusions and insights from your data.
  • Machine learning: Data analysis software can enhance data insights using SQL as a query language, which speeds up the transformation of data to insights.

What are Alternatives to Big Data Processing Software?

There are a few alternatives to big data processing which can be used in collaboration with data processing software or separately.. Here are some examples of what these include:

  • Data warehouse software: Companies who have large, siloed data sources may use data warehouse software to house their data in multiple databases and use business intelligence tools to pull data into one space.
  • NoSQL databases: These types of databases help store more loosely structured or unstructured data. This is more useful for companies who collect different forms or types of data that’s both structured and unstructured.  

If you’re looking for a solution to connect your disparate data sources and make the most of your data, SolveXia is the answer! SolveXia makes it possible to unify your data systems and spreadsheets without manual effort.

With connected data, you can leverage automated reports to make better decisions and utilise process automation to streamline day-to-day business processes.

What are the Types of Data Processing?

As the value of data processing has been made clear, it’s good to understand the different types of data processing that can be performed. Take a look at what they include (and you’ll see how data processing software makes it all easier and quicker to handle):

  • Manual Processing: Before the era of technological data processing solutions, manual processing was the norm. However, since it entails collecting, filtering, sorting and calculating data without the use of any software or automation tools, it is exceptionally time-consuming, inefficient, and error-prone.
  • Real-Time Processing: This type of processing is able to compute data as soon as data comes in. The benefit here is that all insights become available on the spot, so it’s best for critical and timely decisions.
  • Batch Processing: This processing involves exactly what its name suggests- it’s when chunks of data which are stored over a period of time then get analysed together (in a batch).
  • Multi-Processing: This is where more than two processors work on the same dataset.
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Wrap Up

Most of what you read here may sound difficult at first, but data processing software isn’t meant to be a confusing topic. In fact, it makes your life easier because you’re able to automate collecting data and analysing it to drive decision making.

If you’re looking for a solution that aids your data processing capabilities, as well as maximises your business’ functions using automation, data processing software like SolveXia could be the perfect fit!

SolveXia can help automate large amounts of your data and help draw meaning through data management and automated reporting. Request a demo today and see how your business can make useful conclusions from your data.


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