Excel for Mac and Big Data: Techniques for Working With Large Datasets

Are you planning to open a business, and do you need a platform to manage all business data and transactions? Microsoft Excel is a popular application that you can use for data management and analysis. 

Excel users with Mac computers are pleased to know that it is possible to use and download Excel for Mac to create, edit, view, and share your spreadsheets. Excel is great if you have a small business, but if your business is growing, you will need a better platform to manage large data. 

This article discusses data analysis and using Excel for Mac and big data, techniques for working with large datasets.

Exploratory Data Analysis

Exploratory data analysis (EDA) is a method for analyzing and investigating a data set. It summarizes the main characteristics of data sets through data visualization.

Data scientists can discover patterns, test hypotheses, create assumptions, and spot inconsistencies by manipulating multiple data sources to achieve answers. It offers a better understanding of data sets, their relationship, and the most appropriate statistical method to use.

Importance of Exploratory Data Analysis in Data Science

The main purpose of EDA in data science is to be able to look at the data model for errors and patterns before making assumptions. EDA provides results and answers about statistical concerns such as standard deviations, confidence intervals, and categorical variables.

Analyzing Data for Large Data Sets

Microsoft Excel’s data analysis is not perfect and comes with limitations. If you intend to use Microsoft Excel spreadsheets for large datasets, expect data to exceed row limits and the possibility of data loss when opening the file.

Using spreadsheets to analyze large datasets can be inefficient and slow. You can use other tools to help with data analysis, such as Microsoft Power Query and Power Pivot. Power Query is intuitive, connects multiple data sources, and enhances query activities.

10 Big Data Tools and Techniques for Large Datasets

Data is an important commodity these days. Data becomes useful only when it has meaning and purpose.

Below are some big data analysis software as alternative to spreadsheets. This software can store, analyze, and report data more efficiently. Some of these are open-source (free), while some are sold but with free trials.

1. Dextrus

Dextrus offers quick insights on data sets, anomaly detection, ease of data preparation, analytics, data validation, etc. It can help with data ingestion, transformation, reporting, and machine learning algorithms.

It also features the Log-based CDC feature. This feature allows real-time data streaming by reading db logs to identify the changes in the source data.

2. Integrate.io

Integrate.io is a platform that can bring all data sources together. The software can prepare, integrate, and process data analytics on the cloud. It offers solutions for sales, marketing, developers, and support through email, phone, online meetings, and chat.

The benefits of using Integrate.io are its scalability and ability to connect with various data stores. It also offers advanced customization and versatility.

3. R

R is the most comprehensive open-source tool for statistical analysis. Statisticians and data miners recommend this. This software is written in C, R, and Fortran programming languages.

R includes use cases such as data manipulation, calculation, analysis, and graphical display. It is known for its unparalleled charts and graphics. The disadvantages of using R include poor memory management, security, and speed.

4. Silk

If you want to integrate heterogeneous data sources, Silk is an open-source data tool that you can use. It is a linked data paradigm-based framework.

5. Adversity

Adversity is a marketing analytics program that helps marketers track marketing efforts and performance, and helps them discover new insights. It can automate data integration, data visualization, and AI predictive analytics.

The best thing about Adversity is that it offers higher growth, measurable ROI, and business decisions backed by data. It is also highly scalable and flexible. It offers customer-driven approach for fast data handling and transformation.

6. Dataddo

Dataddo is a non-coding and cloud-based Extract, Transform, and Load (ETL) platform. This platform creates simple, stable, and fast data pipelines.

It has an intuitive interface that easily plugs into the existing data stack. It is user-friendly for non-technical individuals. It is no longer necessary to add elements that you are not using or make changes to the workflow.

7. Apache Hadoop

Apache Hadoop is the topmost big data tool used by several Fortune 50 companies such as Amazon, IBM, Facebook, etc. It is an open-source software for managing large data and clustered file systems.

This software is written in Java and provides support for cross-platform. It uses the MapReduce programming model to process big data sets. The best thing about this software is its ability to store and manage various data types – images, videos, XML, and plain text.

8. Cassandra

Cassandra is another open-source distributed NoSQL and DBMS constructed. It is free to use and can manage large volumes of data across various servers.

One advantage of using Cassandra is its ability to quickly handle and process huge volumes of data. It has no single point of failure and has log-structured storage. However, it may need improvements in maintenance and troubleshooting.

Big companies such as American Express, Yahoo, Facebook, and Accenture use Cassandra to manage their data.

9. KNIME

KNIME, or Konstanz Information Miner, is a free and open-source data tool considered a better alternative to SAS. The best thing about this tool is its ability to integrate well with other technologies and languages.

It is used for several functions, such as:

  • Enterprise reporting, research, and integration
  • CRM
  • Data analytics
  • Data mining
  • Business intelligence

It is highly usable, easy to set up, has no stability issues, automates manual work, and efficiently organizes workflow. Canadian Tire and Johnson & Johnson are some of the big companies that use KNIME.

10. Datawrapper

If you are looking for a platform for data visualization, Datawrapper is a good pick. Datawrapper is an open-source tool that uses data visualization to help users quickly create embeddable, accurate, and simple charts.

Datawrapper is user-friendly and can be accessed on several devices, such as mobile phones, tablets, or desktop computers. The platform is fast, interactive, fully responsive, and requires zero coding.

It has a great customization feature and has export options available. Some big names that use Datawrapper are Fortune, Twitter, Times, and Bloomberg.

Conclusion

Microsoft Excel is a go-to program for data storage and analysis. However, it comes with some limitations. The program cannot efficiently store and process large volumes of data. Hence, daily work will be inefficient and often delayed, which can be expensive for your business.

The solution to this is to use big data tools. There are several big data tools in the market. Many of these are open-source tools, and some are paid tools. You should first test the software by availing of the free trial. Use the free trial with your existing customers to check if it fits your business well.

 

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