Apache Spark

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The new Apache Shark, also known as Apache Spark, big data solutions have ignited the spark in data analytics. The digital marketing world needs an efficient solution for data management and storage, as well as a meticulous analytic process for better marketing strategies and digital marketing solutions. The Apache Spark provides an open source platform for data management and analytics, and is known to replace the popular Hadoop for big data processing.

The promising Apache Shark big data solutions

Originally developed in the AMP Lab at UC Berkeley, Apache Shark provides a computing framework for data processing and analytics that best serve to address the velocity aspect of big data processing. Its open source platform offers an opportunity for more brilliant schemes in analytics and processing of big data which helps to improve its data processing efficiency that runs 100 times faster than Hadoop. Many digital marketers find Apache Shark as a viable solution for big data analytics for their top level projects.

The lightning-fast cluster data computing

Apache Spark is coined as the lightning-fast cluster data computing processing as it runs its platform on an advanced DAG execution engine that provides support in data cycle flows and in-memory computing. You can run programs and memory 10 times faster than on a disk. The platform runs on a parallel and data distributed process that offers a simple abstraction of the programs with strong cache and persistence capabilities. The developers of the program can also run various programming languages like Java, Python and Scala. You can find more room for iterative processing to run large data sets on the program.

Features of Apache Spark in data processing and analytics

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1. The Resilient Distributed Dataset (RDD)

One of the features of Apache Spark is the Resilient Distributed Dataset or RDD which offers a parallel and fault tolerant structures for data that allows the user to integrate memory and optimize the data replacement for efficient manipulation. This feature is built with failure in mind and it provides a feature to reconstruct the data and information that are lost. It has data lineage feature that keeps the instructions for data transformation together with persistence.

2. Work in variable storage environments

Marketers will find the Apache Shark a viable solution for pinning large data on the memory for more efficient and lightning performance in data processing. You can also combine SQL and streaming for complex data analytics screening in the same application. If you are a fanatic about working in a Hadoop environment, the Apache Shark allows you to operate in its computing framework for advanced analytics processing on the Hadoop storage environment. The user can also use the program with other storage systems that are Hadoop supported like Input Format files, Sequence Files and Text Files.

3. Efficient data processing techniques for analytics

The Apache Spark also provides for a high performance computing technique to integrate advanced analytics of big data. It is designed to support in-memory processing to allow the users to develop various iterative algorithm programs without the need of writing out the program each time it is passing through data. As a result, it is viewed to deliver 100 times faster when compared from the other algorithms used in MapReduce.

The platform is also integrated with an advanced analytic technique using machine learning library (MLib), Graph X (a graphics engine) and the Spark Streaming query tool Shark for faster query tool in streaming analytics. The users can utilize these tools for more efficient big data analytics.

4. Dstreams abstraction feature

The Dstreams or discretized streams of Spark streaming feature produces an abstraction of sequences of Resilient Distributed Dataset (RDD) data stream which reflects the live incoming data. The Apache Shark receives big data which it divides into batches. It then replicates the data for fault tolerance and allow them to persist in the memory to make them available for algorithmic or mathematical processing. Once processed, the data becomes available in time-burst interval for RDD and further processed using the Spark applications. Programming interfaces like Python, Java and Scala are supported by Apache Shark.

5. Ease of use applications and programming platforms

Apache Shark is getting more popular in the digital marketing age because it is easier to use and manage. Its application is downloadable and runs smoothly even on a laptop. It also runs together with other application frameworks with seamless operation with Spark Streaming, Spark Mlib, Graph X, Spark SQL and Hadoop data. For the current users of Apache Spark, they claim that the program does fix some oversights that are seen in Hadoop.

Safeguarding big data solution in evolving data processing technologies

Owing to the emerging different data processing solutions, tools and technologies for data analytics, integrating a data protection program within your organization is essential to prevent data security breaches. Data protection involves more than just a technical challenge for marketers. It is also intertwined with privacy, staffing and legal issues. Here are some of the means of safeguarding big data as you explore various big data technologies for more efficient data management, processing and analytics within your organization:

  • Devise a big data backup plan in case you experience using faulty data processing solutions. There are cloud services that offer a viable online backup data solution to provide consistent data protection at all times.
  • Use data storage applications that are reliable in storing the volumes of data as they come. Limit the access on these data to a few staff with expertise in data management and analytics to check the overall performance of your automated big data processing programs.
  • Implement a big data disaster recovery plan. It is best to use data processing software that can provide a storage backup in case something goes awry in the data processing. Using Apache Spark offers a failure proof backup for the integrated data storage to keep them safe.
  • Regularly monitor the big data activity from the data processing and analytics tool that you are using, keeping an on on both the application performance and user activities to identify big data privacy violations.
  • Integrate your data analytics software with data security management tools.
  • Invest in a reliable cybersecurity software that will protect your data against vulnerabilities to third party data system breach.

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