Handling of Big data

Effective handling of Big data

Below are some tips to effectively handle big data:

• Define your goals
  It is crucial to decide your goal, essential for data collection. If we don’t have our goals determined, then this can lead to unnecessary data collection, which is not needed. Therefore, adequately defined plans can help in relevant data collection.
   
• Don’t download the data
  Big data is voluminous, and hence it is not recommended to download it on your local machines. You can use servers to download and store them. In servers, you can download the data using a URL. Refer to downloading data using wget here.
   
• Secure, and protect your data
  It is crucial to secure your data from malware and protect it by spam filtering, firewall security measures, etc. Data is prone to threats from human and synthetic anomalies. Therefore it is essential to safeguard your data. Cloud storage is a great way to store your data.
   
• Data should be interlinked
  There should be no issues in communication between data and applications. Cloud storage is an excellent way for data interlinking. A remote database administrator is helpful for seamless data synchronization. This helps in the case when multiple teams need to access the data.
   
• Processing
  Hadoop, MapReduce, etc., are some of the great tools that help in data storing and processing.
   
• Analysis and querying
  PIG, Platfora, WibiData are some of the great tools useful in data analysis and querying.
   
• Business intelligence
  Business Intelligence refers to business analytics, visualization, and best practices to make data-driven business decisions. Hive is an excellent tool for Business Intelligence. Visualization is critical as images can quickly help us analyze the data. For example, during one experiment, a group of researchers plotted the gene content, and they encountered multiple zeroes. They could figure out where the algorithm was failing and then correct it.
   
• Machine learning
  Apache Mahout, SkyTree, etc., are some of the great tools helpful for machine learning with big data.
   
• Store metadata, show your workflow, and use version control
  Metadata means the data which provides information about other data. It is crucial to show your workflow and store the metadata as it leads to clarity for others who study your work. It accounts for more credibility. Using version control can help you keep track of the changes in your documents.
   
• Automate
  As big data is voluminous, it is highly recommended to automate the data processing as it can consume precious time.
   
• Make computing time count
  Nowadays, there are multiple High Performing Computing options available. In research, time is money. Therefore it is essential to optimize your process to save time.
   
• Capture your environment
  Saving your environment using docker, etc., can save time. This can enable replicating your process with ease even after ten years.

Frequently Asked Questions

1. What are the characteristics of big data?
Some characteristics of big data are volume, velocity, variety, value, etc.

2. What are the ways of handling big data problems?
Effective big data handling includes outlining your goals, securing, processing, and querying data, properly storing data, and automating the process.

3. How do you approach a large data set?
For large datasets, we can analyze continuous variables such as age by calculating mean, median, and standard deviation, and for nominal variables such as gender, we can calculate percentages.

4. How is big data collected and stored?
Big data can be collected from surveys, transaction details, call records, social networking sites, etc. Apache Hadoop is a famous tool for storing large datasets.

5. What are some helpful big data tools?
Apache Hadoop, Cassandra, MongoDB, Knime, Datawrapper, etc.,  are some famous big data tools.

Conclusion

This article discussed the handling of Big data.

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Also, check out - Anomalies In DBMS.

Happy Coding!