Introduction to Apache Spark
Brands and businesses around the world are pushing the envelope, when it comes to strategies and growth policies, in order to get ahead of their competition in a successful manner. One of these techniques is called data processing which is today playing a very important and integral role in the functioning of brands and companies. With so much data present within companies, it is important that brands can make sense of this data in an effective manner.
This is because data has to be a readable manner making it easier to gain insights from them. Companies also need a standardized format so that they can process information in a simple and effective manner. With data processing, companies can face hurdles in successful fashion and get ahead of their competition as processing can help you concentrate on productive tasks and campaigns. Data processing services are able to handle a lot of non-core activities including conversion of data, data entry and of course data processing.
Data processing allows companies to convert their data into a standard electronic form. This conversion allows brands to take faster and swifter decisions thereby allowing brands to develop and grow at a rapid pace than before. When brands can focus on things that matter, they can develop and grow in a competitive and successful manner. Some services that come under data processing includes image processing, insurance claims processing, check processing and form processing.
While these may seem like minor issues within a company, they can really improve your value in the market. When consumers and clients can access information in an easy and secure manner, they will be able to build brand loyalty and power in an effective manner. Form processing is one way in which brands can make information available to the bigger world. These forms include HTML, resumes, tax forms, different kinds of surveys, invoices, vouchers, and email forms.
One of the basic transaction units for all companies is a check and it is the basis for all commercial transactions and dealings. With the help of check processing, brands can ensure that their checks are processed in a proper manner and that payments are made on time, thereby helping brands to maintain their reputation and integrity as well. Insurance is another element that plays an important role in the functioning of brands as it helps companies to reimburse their losses in a fast and secure manner.
When you invest in a good insurance processing plan, brands can save time and effort while at the same time continue with their job duties and responsibilities. Image processing might seem like a minor task but at the same time can take a brand’s marketing strategy to the next level. Making high-quality images is extremely important and when brands put such images in their brochures and pamphlets, they automatically attract the attention of clients and customers in an effective manner.
Stages of Data Processing Cycle
Data processing goes through six important stages from collection to storage. Here is a brief description of all the stages of data processing:
1. Collection
Data has to be collected in one place before any sense can be made of it. This is a very important and crucial stage because the quality of data collected will have a direct impact on the final output. That is why it is important that data collected at all stages is correct and accurate because they will have a direct impact on the insights and findings. If the data is incorrect at the beginning itself, the findings will be wrong and the insights gained can have disastrous consequences on brand growth and development. A good collection of data will ensure that the findings and targets of the company are right on the mark. Census (data collection about everything in a group or a particular category of the population), sample survey (collection method that includes only a section of the entire population) and administrative by-product are some of the common types of data collection methods that are employed by companies and brands across all sections.
2. Preparation
The second stage of data processing is preparation. Here raw data is converted into a more manageable form so that it can be analyses and processed in a simpler manner. The raw form of data cannot be processed as there is no common link among them. In addition, this data must be checked for accuracy as well. The preparation of data involves the construction of a dataset that can be used for the exploration and processing of future data. Analyzing data is very important because if the wrong information seeps into the process, it can result in the wrong insights and impact the entire growth trajectory of the company in a very wrong and negative manner.
3. Input
The third stage of data processing is called input where verified data is coded or converted in a manner that can be read in machines. This data, in turn, can be processed in a computer. The entry of data is done through multiple methods like keyboards, digitizers, scanner or data entry from an existing source. Although it is a time-consuming process, the input method requires speed and accuracy as well. The data requires a formal and strict syntax method as the processing power is high when complex data needs to be broken down. That is why companies feel that outsourcing at this stage is a good idea.
4. Processing
In this stage, data is subjected to a lot of manipulations and at this point, a computer program is executed where there are a program code and tracking of current activities. This process can contain multiple threads of execution that execute instructions in a simultaneous manner, depending on the operating system. While a computer is just a group of instructions that are passive, a process is the actual execution of these instructions. Today, the market is filled with multiple software programs that process huge quantities of data in a short period of time.
5. Output and Interpretation
This is the fifth stage of data processing and it is here that data is processed information and the insights are then transmitted to the final user. The output can be relayed in various formats like printed reports, audio, video or monitor. The interpretation of data is extremely important as this is the insights that will guide the company on not just achieving its current goals but also for setting a blueprint for future goals and objectives.
6. Storage
The storage is the final stage in the data processing cycle where the entire process above, meaning the data, instruction, and insights is stored in a manner that they can be used in the future as well. Data and its relevant insights must be stored in such a manner that it can be accessed and retrieved in a simple and effective manner. Computers and now systems like the cloud can effectively hold vast amounts of data in an easy and convenient manner, making it the ideal solution.
After establishing the importance of data processing, we come to one of the most important data processing units, which is Apache Spark. Spark is an open-source cluster computing framework that was developed by the University of California. It was later donated to the Apache Software Foundation. As against Hadoop’s two-stage disk-based MapReduce paradigm, Spark’s multi-stage primitives provide great speed for performance.
Role of Apache Spark
There are many things that set Spark apart from other systems and here are some of the following:
Apache Spark has automatic memory tuning
It has provided a number of tunable knobs so that programmers and administrators can use them to take charge of the performance of their applications. As Spark is an in-memory framework, it is important that there is enough memory so that actual operations may be carried out on one hand and have sufficient memory in the cache on the other hand. Setting the correct allocations is not an easy task as it requires a high level of expertise to know which parts of the framework must be tuned. The new automatic memory tuning capabilities that have been introduced in the latest version of Spark, making it an easy and efficient framework to use, across all sectors. Additionally, Spark can now tune itself automatically, depending on the usage.
Spark can process data at a lightning-fast pace
When it comes to Big Data, speed is one of the most critical factors. Despite the size of the data being large, it is important that the data frame is able to adjust with the size of data in a swift and effective manner. Spark enables applications in Hadoop clusters to function a hundred times faster in memory and ten times faster when data runs on the disk. This is possible because Spark reduces the number of read/write to disc and as apache spark framework stores this intermediate processing data in-memory, makes it a faster process. By using the concept of Resilient Distributed Datasets, Spark allows data to be stored in a transparent manner on the memory disc. By reducing the time to read and write on a disc, data processing becomes faster and improved than ever before.
Spark supports a lot of languages
Spark allows users to write their applications in multiple languages including Python, Scala, and Java. This is extremely convenient for developers to run their applications on programming languages that they are already familiar with. In addition, Spark comes with a built-in set of nearly 80 high-level operators as well which can be used in an interactive manner.
Spark supports sophisticated analytics
Besides a simple map and reduce operations, Spark provides supports for SQL queries, streaming data and complex analytics such as machine learning and graph algorithms. By combining these capabilities, Spark allows users to work in a single workflow as well.
Spark allows the real-time streaming process
It allows users to handle streaming in real-time. Apache Spark Mapreduce mainly handles and processes the stored data while Spark manipulates the data in real-time with the use of apache spark Streaming. It can also handle frameworks that work in integration with Hadoop as well.
Spark has an active and expanding community
Build by a wide set of developers that spanned more than 50 companies, Apache is really popular. Started in the year 2009, more than 250 developers around the globe have contributed to the growth and development of Spark. It also has an active mailing list and JIRA for issue tracking.
Spark can work in an independent manner as well as in integration with Hadoop
Spark is capable of running in an independent fashion and is capable of working with Hadoop 2’s YARN cluster manager. This means that it can read Hadoop data as well. It can also read from other Hadoop data sources like HBase and HDFS. This is why it is suitable for brands that want to migrate their data from pure Hadoop applications. As Spark uses immutability, it might not be ideal for all cases of migration.
It has been a major game-changer in the field of big data since its evolution. It has been probably one of the most significant open-source projects and has been adopted by many companies and organizations across the globe with a considerable level of success and impact. Data processing has many benefits for companies that want to establish their role in the economy on a global scale. By understanding data and gaining insights from them, it can help brands to create policies and campaigns that will truly empower them, both within the company and outside in the market well. This means that data processing and software like Apache Spark can help companies to make use of opportunities in an effective and successful manner.
In conclusion, Spark is a big force that changing the face of the data ecosystem. It is built for companies that depend on speed, ease of use and sophisticated technology. It performs both batch processing and new workloads including interactive queries, machine learning, and streaming, making it one the biggest platforms for growth and development of companies around the world.
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This has been a guide to Apache Spark. Here we have discussed the basic concept, stages and roles of Apache Spark with detail explanation. You may also look at the following articles to learn more –
