Big Data is a part of Data Science that is based on the volume, velocity and variety. It is just the unfathomed data, flowing from various sources, which has the immense potential to propel just about any industry. This large chunk of scattered and unstructured data lying in the digital universe requires proper management and analysis to derive meaningful information. We need technically advanced applications and software that can utilize fast and cost-efficient high-end computational power for such tasks. This is the entry point of Big Data Frameworks.
How can Big Data help you?
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- Big data is being used for understanding and targeting customers by e-commerce companies. It is being used to understand and optimize business processes. People may use it to get rich insights and real value in analyzing the collective data. Collecting big data empowers healthcare professionals in computing analytics and predicting disease patterns.
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- Big Data Techniques help monitors sick and immature babies. Such units can now predict infections, check heartbeat and breathing patterns 24 hours before the occurrence of any physical symptoms.
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- Big Data Analytics also helps in monitoring and predicting the developments of epidemics and disease outbreaks.
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- Big Data Scientists and developers have been integrating data from medical records with social media analytics enabling us to monitor flu outbreaks in real-time.
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- Big Data is huge in improving sports performance. Sports teams make use of video analytics to track the performance of every player in a football or baseball game. The sensor technology guides them to improve their game. Such devices, equipped with data science techniques even monitor athletes outside the sporting environment to track their sleep and food intake.
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- The computing power of big data can be applied to any set of data opening up new sources to scientists who wish to go forward with more research and findings.
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- Big Data also helps optimize computer and data warehouse performance, to improve security and law enforcements – this worked on the idea of empowering individuals with certain information about job training programs pr let them know about increased penalties for people with certain backgrounds. (It was however different from practice profiling).
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- Big Data can also help regularize the traffic flows by optimizing based on social media and weather data.
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- Computers can be programmed with Big Data complex algorithms that scan markets for a set of customizable conditions and search for trading opportunities.
Few Big Data Examples
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- Discovering consumer shopping habits
- Predictive inventory ordering
- Promotions based on the consumer buying behaviour and purchase history
- Customized marketing
- Fuel optimization tools for the transportation industry
- Monitoring health conditions through data from wearables
- Live road mapping for autonomous vehicles
- Real-time data monitoring and cyber-security protocols
- Streamlined media streaming
- Personalized health plans for cancer patients
What is Big Data, by definition and which technologies does it support?
Big Data refers to collecting large complex data sets, which are often unstructured and are often difficult to process using traditional applications/tools. Amongst various frameworks available to handle big data, some important ones include Apache Hadoop, Microsoft HDInsight, NoSQL, Hive, Sqoop, Polybase, Big Data in Excel, Spark and Presto. As relevant to the current discussion, here is a featured comparison of two traditional big data frameworks are Hadoop vs. Spark:
The Difference Between Spark And Hadoop
Comparison |
Apache Hadoop |
Apache Spark |
| What is it? |
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| Type of data processing |
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| Cost |
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| Performance |
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| Ease of Use | Hadoop is scalable, reliable and easy to use. |
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| Security | Authentication is carried out with Kerberos and third-party tools on Hadoop. The third-party authentication options for Hadoop include Lightweight Directory Access Protocol. Security measures also apply to the components of Hadoop. For HDFS, for example, access control lists, as well as traditional file permissions, are applied. | Spark has file-level permissions and access control lists of HDFS since Spark and HDFS can be integrated. |
| Fault Tolerance | Hadoop deals with fault tolerance in two ways: (1) through the qualitative control function of the master daemons, as well as with commodity hardware.
(2) Community hardware is applied by Hadoop in replicating data when failures occur. The master daemons of the two components of Hadoop monitor the operation of the slave daemons. When a slave daemon fails, its tasks are assigned to another functional slave daemon. |
Spark makes use of Resilient Distributed Datasets (RDDs) which help in checking the failures by referring to datasets shared in external storage systems. Thus, RDDs can keep datasets accessible, in memory, across operations. RDDs can also be recomputed when they are lost.
Since RDDs are involved in fault tolerance in Spark when failures occur, minimal downtime is experienced, and operation time is not significantly lengthened. |
Apache Spark vs. Hadoop: Where should we go from here?
The ever increasing data that is exponential with the growth of the population must be followed up with tools that meet the expanded need for data analytics. We discussed Apache Spark and Apache Hadoop in this space and got to know that Spark is more expensive to use than Hadoop, the details of projects could be modified to fit a wide range of budgets. Both these two tools are trusted by some of the biggest companies and App Developers India in the tech space. These are sustainable and suitable for different kinds of projects. Hadoop covers a wide market followed by Spark.