Big data tools are the software applications and platforms which are used to collect, store, process, analyze, and visualize large and complex dataset. They help organizations gain insights from massive amounts of data that traditional methods cannot process. These big data tools are very much used to help in every level of the data analysis stage right from storing unstructured data to generating reports, predictions, etc. Businesses, researchers, and analysts use these tools to gain insights, improve decision-making, and enhance performance.
What is Big Data Tools?
Big data tools are made to manage high volumes of data or info from different sources (places) at high speed. These tools serve different purpose as storage, real time processing, batch processing, data analysis, data visualization etc. They can efficiently use structured, semi-structured, and unstructured data.
Big data analytics tools are numerous as well, such as, but not limited to, Apache Hadoop (storage), Apache Spark (processing), or Tableau (visualization). The development of the modern data analytics tools for big data has led businesses to spot patterns in data, track user behavior and predict future trends more quickly and accurately than before.
Big Data Tools
Here are the ten most popular big data analytics tools that manage and analyze large data sets: Since each tool has its own purpose as well as features, every tool is best suited for particular big data operations.
Xplenty
It is an analytics tool used to build a data pipeline, with simple coding for integration. It covers everything from sales to marketing and support. It provides ETL, ELT, etc solutions and an interactive graphical interface. Xplenty is the best tool for you if you want a very low investment in hardware & software, and want support via email, chat, telephonic or via virtual meetings. Xplenty is a cloud-based data processing platform thatorchestrates all data in one place.
Features of Xplenty:
- Rest API is the thing by which a user can do anything.
- It provides SSL/TSL (which is used for the basic security functions) as well as being able to validate algorithms and certificates on the platform on a regular basis.
- It provides integration apps for both in-house & cloud, and also provides deployment for cloud integrations.
Spark
All frameworks used for data processing and perform many tasks at scale, Apache Spark is one of that. It is also used to deal out data through different computers with the guidance of distributing instruments. It is extensively employed in data analysts as it contains user-friendly and easy to utilize APIs that serves various data-pulling methods and is efficient for multi-petabytes of data processing as well. Recently, it was established a new world record for a Spark process of 100 terabytes of data in 23 minutes, breaking the previous record of Hadoop (71 minutes).
Features of APACHE Spark:
- It also enables users to be able to run in the language of their choice. (JAVA, Python, etc.)
- (Asynchronous) Input: Spark can process real-time streaming using Spark Streaming
- It can run on Mesos, Kubernetes, or the Cloud.
Apache Hadoop
It is a Java-based open-source platform that is used to store and process big data. It is based on a clustered model that allows simultaneity, i.e., running processes to execute parallel, processing data faster. It is capable of processing both structured and unstructured data from a single server to several computers. Apart from that, Hadoop provides its users with cross-platform support. Today it is the best big data analytics tool, and it is widely used among tech giants like Amazon and Microsoft, IBM, etc.
Features of Apache Hadoop:
- Use without charge and provides an effective storage system for organisations.
- Provides Fast access through HDFS (Hadoop Distributed File System).
- Very malleable and quick to implement using MySQL and JSON
- Typical distribution for a huge amount of data in small fragments.
Cassandra
APACHE Cassandra is an open-source NoSQL distributed database that fetches large chunks of data. It is known as one of the most preferred tools for data analytics and has been admired by numerous tech companies because of its highly scalable and available with no degradation of speed and performance. It can perform thousands of transactions per second, scales to petabytes of resources, and provides near-zero downtime. And it was first developed by Facebook in 2008, when it was made available for public use.
Features of APACHE Cassandra:
- It can handle all types of data namely structured, unstructured, semi-structured and also lets users modify according to their business requirement.
- Easy to distribute data as you can replicate your data on different data centers.
- Cassandra can be run on cheap commodity servers and provides both high storage and high data throughput.
Qubole
An open-source big data tool to fetch data in a value chain using ad-hoc analysis in machine learning. Qubole is a data lake platform that provides an end-to-end service with greatly minimized time and efforts which are needed to move data pipelines. You are Domain agnostic and can provision multi cloud services like AWS, Azure, Google cloud. Another incentive is that it also reduces the cost of cloud computing by 50%.
Features of Qubole:
- Support for ETL process: Allows companies to migrate their data in one place from multiple sources.
- Real-time Insight: It keeps a tab on user’s systems and enables them to check real-time insights
- Predictive Analysis: Qubole has predictive analysis thus companies can work accordingly to do more acquisitions targeting.
Mongo DB
Came in light in 2010 as, free, open-source platform and document-oriented (NoSQL) database used to store high volume of data. It stores data in collections and documents, where a document combines key-value pairs treated as a minimal unit of Mongo DB. These are available for multi-programming languages like Python, Jscript, and Ruby which explains its immense popularity among developers.
Features of Mongo DB
- Written in C++: Its an schema less DB which can contain different documents inside.”
- Stack Simplification: With mongo, a user can effortlessly store any number of files in stack without any disturbance.
- Master-Slave Replication: It can write/read data from master and can be called back for backup.
SAS
Today, it’s one of the top tools Data Scientists use to build statistical modeling. SAS – A data scientist can manage, mine, update or extract data in various forms from diverse sources using SAS. The SAS (Statistical Analytical System) can allow a user to view the data in any form (SAS tables or Excel worksheets). Other than that it also provides cloud platform for business analytics named SAS Viya and to get strong hold on AI & ML.
Features of SAS
- It can also have extensive libraries, making it very useful for non-programmers as it has easy learn syntax
- It has built-in support for a wide array of languages (including SQL) and the ability to read from virtually any format.
- It enables end-to-end security through a feature known as SAS/SECURE.
Data Pine
Datapine is not BI and EC analytical, which has been established since 2012 (Berlin, Germany). It is used for the data extraction (for small-medium companies to extract data for close follow up) and it has achieved good popularity in some of countries in very short time. All this with its improved UI design, offering to visit and inspect the data as and when required in 4 price brackets ranging from $249 per month.
Features of Datapine
- datapine contains tons of AI assistants and BI tools to minimize the manual chase.
- The forecasting/predictive analytics features of Datapine utilize previous data alongside the current information to extract future results.
- Some BI tools like datapine have many AI assistants available to them to eliminate manual chase.
- Datapine uses historical and current data to predict future outcomes, so it enables forecasting/predictive analytics.
Rapid Miner
It is a fully automated visual workflow design platform for data analytics. It is a no-code platform, that does not require coders to segregate data. Today it is heavily used with industries like ed-tech, training, research, etc. But it is an open-source platform it has a limitation of adding 10000 data rows and 1 logical processor. Data scientists can make use of the Rapid Miner to deploy their ML models over web or mobile (only when the user interface is ready to capture running figures).
Features of Rapid Miner
- Accessibility: it can provide end-user accessibility to 40+ files types (SAS, ARFF, …) via URL
- Cloud: Users can also access cloud storage facilities like AWS and dropbox
- It also enables faster evaluation of data as you can visualize several results at the same time in history.
Relevance to ACCA Syllabus
The ACCA syllabus gives prominence to the use of Big data analytics tools in developing information for Strategic Business Leaders (SBL) and Advanced Performance Management (APM). Some common features in analytical tools are assessing financial trends, improving decision-making, and identifying fraud. Such platforms include Power BI, Tableau, Hadoop, and SQL used to generate insights and data-driven financial operations and enhance the quality of a digital audit.
Big Data Tools ACCA Questions
Q1: What are you most excited about in big data for finance?
A) Tableau
B) Excel Solver
C) Tally
D) WordPad
Ans: A) Tableau
Q2: How is SQL mostly used in accounting analytics?
A) For an efficient query and management of large financial databases
B) PowerPoint presentations.
C) Creating logos for marketing
D) To manually calculate payroll
Ans: A) For efficiently querying and managing large financial databases
Q3: Which prominent big data technology is used for processing large datasets through parallel computing?
A) Apache Hadoop
B) Google Docs
C) QuickBooks
D) Excel Macros
Ans: A) Apache Hadoop
Q4: Microsoft Power BI: Accountants LOVE IT!
A) Interactive Financial Dashboards and Data Visualisation in Real time
B) Handwritten notes feature
C) Reentering the manual journal entry
D) Non-editable reports
Ans: A) Financial dashboards that are interlinked and real-time data visualization
Q5: · Is a cloud based data analysis platform connects to the excel and power point.
A) Microsoft Power BI
B) Adobe Illustrator
C) Notepad++
D) Microsoft Paint
Ans: A) Microsoft Power BI
Relevance to US CMA Syllabus
The use of big data tools in Performance Management, Decision Analysis, and Cost Management is present in the US CMA syllabus. Common tools used by CMAs for variance analysis, budgeting, forecasting, and benchmarking are Tableau, R, Python and SAS. They provide accurate tools and practical insights to help managers in their decision making process.
Big Data Tools CMA Questions
Q1: In cost analysis, which big data tool is mainly used for statistical modeling and forecasting?
A) R
B) Excel Text Box
C) MS Paint
D) Google Translate
Ans: A) R
Question 2: What role does Tableau play in measuring performance?
A) Real time dashboards with visual analitics and KPIs
B) It generates reports limited to text
C) It denies access to management reports
D) It conceals performance metrics
Ans: A) Real time dashboards with KPI and visual analytics
Q3: What programming language is commonly used with big data tools to automate tasks or create statistical models for forecasting potential market moves?
A) Python
B) HTML
C) PHP
D) Swift
Ans: A) Python
Q4: How does SAS help CMAs make informed, data-driven decisions?
Enhance analytics and reporting capabilities for large datasets.
B) Graphic designing
C) Manual cashbook entries
D) Slide presentations
Ans: (A) Analytical and reporting capabilities at scale
Q5: What data export format is commonly used for financial data with big data tools?
A) CSV(Comma Separated Values)
B) JPG
C) MP3
D) ZIP Password Protected
Ans: A) CSV (Comma Separated Values)
Relevance to US CPA Syllabus
Audit & Attestation (AUD) and Business Environment & Concepts (BEC) cover big data tools in the US CPA syllabus. Accounting firms can use tools like IDEA, ACL, and SQL to analyze audit evidence, detect fraud patterns and manage large transaction datasets easily. In the US CPA syllabus, the focus on big data tools is spread out in Audit & Attestation (AUD) as well as Business Environment & Concepts (BEC).
Big Data Tools CPA Questions
Q1: What is the purpose of IDEA for auditors?
A) Searching through massive quantities of transactional data for anomalies
B) Designing email campaigns
C) Managing employee benefits
D) Scanning handwritten notes
Ans: A) Analyzing the huge chunks of transactional data for irregularities
Q2: What is the best big data tool for automated audit sampling?
A) ACL (Audit Command Language)
B) Photoshop
C) Tally ERP
D) QuickBooks Pro
Que: Which of the following is not a specific type of continuous audit procedure? Ans: A) ACL (Audit Command Language)
Q3: What role can SQL play in helping CPAs work on audits?
A) In by analysing financial records for anomalies
You are mystified by making bar charts manually.
C) By encrypting password files
D) Designing audit brochures
Ans: A) Analyzing financial records to find anomalies
Q4: What is the most common reason for employing the use of big data tools in audits?
A) Speeding up risk identification and validation of data
B) Full manual audit trail documentation
C) Poorer quality of sample testing
D) Delayed financial reviews
Ans: A) Risk identification and Data validation speedup
Q5: Which of the following is a cloud-based data analytics platform utilized by CPAs?
A) CaseWare IDEA Cloud
B) Notepad Audit
C) Excel Printed Ledger
D) MS Word for Audit
Ans: A) CaseWare IDEA Cloud
Relevance to CFA Syllabus
Big data tools in the CFA curriculum are relevant to Equity Investments, Portfolio Management, and Quantitative Methods. CFA professionals use tools such as Python, R, Power BI, and Bloomberg Terminal to do financial modelling, risk assessment, and sentiment analysis out of big datasets and unstructured data sources.
Big Data Tools CFA Questions
Q1: On what platform are financial market data and analytics offered in real time?
A) Bloomberg Terminal
B) Google Slides
C) Acrobat Reader
D) CorelDRAW
Ans: A) Bloomberg Terminal
Q2: What is something you like the best about using python when it comes to portfolio analytics?
A) Automates risk modelling and quantitative analysis
B) Enhances slide design
C) Converts PDFs to Excel
D) Limits data entry
Ans: A) Quantitative analysis & automated risk modelling
Q3: What big data tools do investment analysts use for sentiment analysis?
A) Scraping news and social media content to extrapolate on market mood.
B) By studying historic book values
C) By periodically updating printed reports
D) By denying behavior of investors
Ans: A) Market sentiment: Scraping news/news & social media for market mood
Q4: Automated portfolio rebalancing is supported by which of the listed tools?
A) Wealthfront (AI-enabled platform)
B) Google Sheets
C) Printed ledgers
D) Notepad++
Ans: A) Wealthfront (AI driven platform)
Q5: How can R help in financial modeling?
A) Investment forecasting using advanced statistical analysis
B) Presentation animations
C) Manual typing practice
D) Risk report printing
Ans: A) Complex statistical analysis for investment prediction
