Search

The Present Day Scope of Undertaking a Course In Hadoop

Hadoop is known as an open-source software framework that is being extensively used for running applications and storing data. Moreover, Hadoop makes it possible to run applications on systems that have thousands of commodity hardware nodes. It also facilitates the handling of thousands of terabytes of data. It is interesting to note that Hadoop consists of modules and concepts like Map-Reduce, HDFS, HIVE, ZOOKEEPER, and SQOOP. It is used in the field of big data as it makes way for fast and easy processing. It differs from relational databases, and it can process data that are of high volume and high velocity. Who should undertake a course in Hadoop? Now a days main question is Who can do hadoop course. A course in Hadoop suits those who are into ETL/Programming and looking for great job opportunities. It is also best suited for those managers who are on the lookout for the latest technologies that can be implemented in their organization. Hence, by undertaking a course in Hadoop, the managers can meet the upcoming and current challenges of data management. On the other hand, training in Hadoop can also be undertaken by any graduate and post-graduate student who is aspiring to a great career in big data analytics. As we all know, business analytics in the new buzz in the corporate world. Business analytics comprises of big data and other fundamentals of analytics. Moreover, as this field is relatively new, a graduate student can have endless opportunities if he or she decides to pursue a training course in Hadoop. Why is Hadoop important for professionals and students? In recent years, the context of pursuing a course in any professional subjects is of due importance. This is the reason that many present day experts are on the lookout for newer methods to enrich their skills and abilities. On the other hand, the business environment is rapidly changing. The introduction of Big Data and business analytics has opened up avenues of new courses that can help a professional in their growth. This is where Hadoop plays a significant role. By undertaking a course in Hadoop, a professional would be guaranteed of huge success. Following are the advantages that a professional would gain while taking a class in Hadoop-  • If a professional takes a course in Hadoop, then he or she will acquire the ability to store and process a massive amount of data quickly. This can be attributed to the fact that the load of data is increasing day by day with the introduction of social media and Internet of Things. Nowadays, businesses take ongoing feedback from these sites. Hence, a lot of data is generated in this process. If a professional undertakes a course in Hadoop, then he or she would learn how to manage this huge amount of data. In this way, he or she can become an asset for the company. • Hadoop increases the computing power of a person. When an individual undertakes training in Hadoop, he or she would learn that Hadoop's computing model; is quite adept at quickly processing big data. Hence, the more computing nodes an individual uses, the more processing power they would have. • Hadoop is important in the context of increasing the flexibility of a company’s data framework. Hence, if an individual pursues a course in Hadoop, they can significantly contribute to the growth of a company. When compared to traditional databases, by using Hadoop you do not have to preprocess data before storing. Hadoop facilitates you to store as much data as you want.  • Hadoop also increases the scalability of a company. If a company has a team of workers who are adept at handling Hadoop, then the company can look forward to adding more data by just adding the nodes. In this case, little supervision is needed. Hence, the company can get rid of the option of an administrator. Additionally, it can be said that Hadoop facilitates the increasing use of business analytics thereby helping the company to have the edge over its rival in this slit throat competitive world. How much is Java needed to learn Hadoop? This is one of the most asked questions that would ever come to the mind of a professional from various backgrounds like PHP, Java, mainframes and data warehousing and want to get into a career in Big Data and Hadoop. As per many trainers, learning Hadoop is not an easy task, but it becomes hassle free if the students are aware of the hurdles to overpower it. As Hadoop is open source software which is built on Java, thus it is quite vital for every trainee in Hadoop to be well versed with the basics of Java. As Hadoop is written in Java, it becomes necessary for an individual to learn at least the basics of Java to analyze big data efficiently.  How to learn Java to pursue a course in Hadoop? If you are thinking of enrolling in Hadoop training, you have to learn Java as this software is based on Java. Quite interestingly, the professionals who are considering learning Hadoop can know the basics of Java from various e-books. They can also check Java tutorials online. However, it is essential to note that the learning approach of taking help from tutorials would best suit a person who is skilled at various levels of computer programming. On the other hand, Java tutorials would assist one to comprehend and retain information with code snippets. One can also enroll for several reputed online e-learning classes can provide great opportunities to learn Java to learn Hadoop. The prerequisites for pursuing a course in Hadoop One of the essential prerequisites for pursuing a course in Hadoop is that one should possess hands-on experience in good analytical and core Java skills. It is needed so that a candidate can grasp and apply the intriguing concepts in Hadoop. On the other hand, an individual must also possess a good analytical skill so that big data can be analyzed efficiently.  Learn more information about how to get master bigdata with hadoop certification  Hence, by undertaking a course in Hadoop, a professional can scale to new heights in the field of data analytics.  
Rated 4.0/5 based on 2 customer reviews

The Present Day Scope of Undertaking a Course In Hadoop

351
The Present Day Scope of Undertaking a Course In Hadoop

Hadoop is known as an open-source software framework that is being extensively used for running applications and storing data. Moreover, Hadoop makes it possible to run applications on systems that have thousands of commodity hardware nodes. It also facilitates the handling of thousands of terabytes of data. It is interesting to note that Hadoop consists of modules and concepts like Map-Reduce, HDFS, HIVE, ZOOKEEPER, and SQOOP. It is used in the field of big data as it makes way for fast and easy processing. It differs from relational databases, and it can process data that are of high volume and high velocity.

Who should undertake a course in Hadoop?

Now a days main question is Who can do hadoop course. A course in Hadoop suits those who are into ETL/Programming and looking for great job opportunities. It is also best suited for those managers who are on the lookout for the latest technologies that can be implemented in their organization. Hence, by undertaking a course in Hadoop, the managers can meet the upcoming and current challenges of data management. On the other hand, training in Hadoop can also be undertaken by any graduate and post-graduate student who is aspiring to a great career in big data analytics. As we all know, business analytics in the new buzz in the corporate world. Business analytics comprises of big data and other fundamentals of analytics. Moreover, as this field is relatively new, a graduate student can have endless opportunities if he or she decides to pursue a training course in Hadoop.

Why is Hadoop important for professionals and students?

In recent years, the context of pursuing a course in any professional subjects is of due importance. This is the reason that many present day experts are on the lookout for newer methods to enrich their skills and abilities. On the other hand, the business environment is rapidly changing. The introduction of Big Data and business analytics has opened up avenues of new courses that can help a professional in their growth. This is where Hadoop plays a significant role. By undertaking a course in Hadoop, a professional would be guaranteed of huge success. Following are the advantages that a professional would gain while taking a class in Hadoop- 

If a professional takes a course in Hadoop, then he or she will acquire the ability to store and process a massive amount of data quickly. This can be attributed to the fact that the load of data is increasing day by day with the introduction of social media and Internet of Things. Nowadays, businesses take ongoing feedback from these sites. Hence, a lot of data is generated in this process. If a professional undertakes a course in Hadoop, then he or she would learn how to manage this huge amount of data. In this way, he or she can become an asset for the company.

Hadoop increases the computing power of a person. When an individual undertakes training in Hadoop, he or she would learn that Hadoop's computing model; is quite adept at quickly processing big data. Hence, the more computing nodes an individual uses, the more processing power they would have.

 Hadoop is important in the context of increasing the flexibility of a company’s data framework. Hence, if an individual pursues a course in Hadoop, they can significantly contribute to the growth of a company. When compared to traditional databases, by using Hadoop you do not have to preprocess data before storing. Hadoop facilitates you to store as much data as you want. 

Hadoop also increases the scalability of a company. If a company has a team of workers who are adept at handling Hadoop, then the company can look forward to adding more data by just adding the nodes. In this case, little supervision is needed. Hence, the company can get rid of the option of an administrator. Additionally, it can be said that Hadoop facilitates the increasing use of business analytics thereby helping the company to have the edge over its rival in this slit throat competitive world.

How much is Java needed to learn Hadoop?

This is one of the most asked questions that would ever come to the mind of a professional from various backgrounds like PHP, Java, mainframes and data warehousing and want to get into a career in Big Data and Hadoop. As per many trainers, learning Hadoop is not an easy task, but it becomes hassle free if the students are aware of the hurdles to overpower it. As Hadoop is open source software which is built on Java, thus it is quite vital for every trainee in Hadoop to be well versed with the basics of Java. As Hadoop is written in Java, it becomes necessary for an individual to learn at least the basics of Java to analyze big data efficiently. 

How to learn Java to pursue a course in Hadoop?

If you are thinking of enrolling in Hadoop training, you have to learn Java as this software is based on Java. Quite interestingly, the professionals who are considering learning Hadoop can know the basics of Java from various e-books. They can also check Java tutorials online. However, it is essential to note that the learning approach of taking help from tutorials would best suit a person who is skilled at various levels of computer programming. On the other hand, Java tutorials would assist one to comprehend and retain information with code snippets. One can also enroll for several reputed online e-learning classes can provide great opportunities to learn Java to learn Hadoop.

The prerequisites for pursuing a course in Hadoop

One of the essential prerequisites for pursuing a course in Hadoop is that one should possess hands-on experience in good analytical and core Java skills. It is needed so that a candidate can grasp and apply the intriguing concepts in Hadoop. On the other hand, an individual must also possess a good analytical skill so that big data can be analyzed efficiently.  Learn more information about how to get master bigdata with hadoop certification 

Hence, by undertaking a course in Hadoop, a professional can scale to new heights in the field of data analytics.
 

Joyeeta

Joyeeta Bose

Blog Author

Joyeeta Bose has done her M.Sc. in Applied Geology. She has been writing contents on different categories for the last 6 years. She loves to write on different subjects. In her free time, she likes to listen to music, see good movies and read story books.

Join the Discussion

Your email address will not be published. Required fields are marked *

2 comments

Sunny Kumar 04 Jan 2018

Nice Post thanks for this sharing

Sundaresh K A 06 Apr 2018

Your post is informative content for hadoop learners.

Suggested Blogs

Apache Spark Pros and Cons

Apache Spark:  The New ‘King’ of Big DataApache Spark is a lightning-fast unified analytics engine for big data and machine learning. It is the largest open-source project in data processing. Since its release, it has met the enterprise’s expectations in a better way in regards to querying, data processing and moreover generating analytics reports in a better and faster way. Internet substations like Yahoo, Netflix, and eBay, etc have used Spark at large scale. Apache Spark is considered as the future of Big Data Platform.Pros and Cons of Apache SparkApache SparkAdvantagesDisadvantagesSpeedNo automatic optimization processEase of UseFile Management SystemAdvanced AnalyticsFewer AlgorithmsDynamic in NatureSmall Files IssueMultilingualWindow CriteriaApache Spark is powerfulDoesn’t suit for a multi-user environmentIncreased access to Big data-Demand for Spark Developers-Apache Spark has transformed the world of Big Data. It is the most active big data tool reshaping the big data market. This open-source distributed computing platform offers more powerful advantages than any other proprietary solutions. The diverse advantages of Apache Spark make it a very attractive big data framework. Apache Spark has huge potential to contribute to the big data-related business in the industry. Let’s now have a look at some of the common benefits of Apache Spark:Benefits of Apache Spark:SpeedEase of UseAdvanced AnalyticsDynamic in NatureMultilingualApache Spark is powerfulIncreased access to Big dataDemand for Spark DevelopersOpen-source community1. Speed:When comes to Big Data, processing speed always matters. Apache Spark is wildly popular with data scientists because of its speed. Spark is 100x faster than Hadoop for large scale data processing. Apache Spark uses in-memory(RAM) computing system whereas Hadoop uses local memory space to store data. Spark can handle multiple petabytes of clustered data of more than 8000 nodes at a time. 2. Ease of Use:Apache Spark carries easy-to-use APIs for operating on large datasets. It offers over 80 high-level operators that make it easy to build parallel apps.The below pictorial representation will help you understand the importance of Apache Spark.3. Advanced Analytics:Spark not only supports ‘MAP’ and ‘reduce’. It also supports Machine learning (ML), Graph algorithms, Streaming data, SQL queries, etc.4. Dynamic in Nature:With Apache Spark, you can easily develop parallel applications. Spark offers you over 80 high-level operators.5. Multilingual:Apache Spark supports many languages for code writing such as Python, Java, Scala, etc.6. Apache Spark is powerful:Apache Spark can handle many analytics challenges because of its low-latency in-memory data processing capability. It has well-built libraries for graph analytics algorithms and machine learning.7. Increased access to Big data:Apache Spark is opening up various opportunities for big data and making As per the recent survey conducted by IBM’s announced that it will educate more than 1 million data engineers and data scientists on Apache Spark. 8. Demand for Spark Developers:Apache Spark not only benefits your organization but you as well. Spark developers are so in-demand that companies offering attractive benefits and providing flexible work timings just to hire experts skilled in Apache Spark. As per PayScale the average salary for  Data Engineer with Apache Spark skills is $100,362. For people who want to make a career in the big data, technology can learn Apache Spark. You will find various ways to bridge the skills gap for getting data-related jobs, but the best way is to take formal training which will provide you hands-on work experience and also learn through hands-on projects.9. Open-source community:The best thing about Apache Spark is, it has a massive Open-source community behind it. Apache Spark is Great, but it’s not perfect - How?Apache Spark is a lightning-fast cluster computer computing technology designed for fast computation and also being widely used by industries. But on the other side, it also has some ugly aspects. Here are some challenges related to Apache Spark that developers face when working on Big data with Apache Spark.Let’s read out the following limitations of Apache Spark in detail so that you can make an informed decision whether this platform will be the right choice for your upcoming big data project.No automatic optimization processFile Management SystemFewer AlgorithmsSmall Files IssueWindow CriteriaDoesn’t suit for a multi-user environment1. No automatic optimization process:In the case of Apache Spark, you need to optimize the code manually since it doesn’t have any automatic code optimization process. This will turn into a disadvantage when all the other technologies and platforms are moving towards automation.2. File Management System:Apache Spark doesn’t come with its own file management system. It depends on some other platforms like Hadoop or other cloud-based platforms.3. Fewer Algorithms:There are fewer algorithms present in the case of Apache Spark Machine Learning Spark MLlib. It lags behind in terms of a number of available algorithms.4. Small Files Issue:One more reason to blame Apache Spark is the issue with small files. Developers come across issues of small files when using Apache Spark along with Hadoop. Hadoop Distributed File System (HDFS) provides a limited number of large files instead of a large number of small files.5. Window Criteria:Data in Apache Spark divides into small batches of a predefined time interval. So Apache won't support record-based window criteria. Rather, it offers time-based window criteria.6. Doesn’t suit for a multi-user environment:Yes, Apache Spark doesn’t fit for a multi-user environment. It is not capable of handling more users concurrency.Conclusion:To sum up, in light of the good, the bad and the ugly, Spark is a conquering tool when we view it from outside. We have seen a drastic change in the performance and decrease in the failures across various projects executed in Spark. Many applications are being moved to Spark for the efficiency it offers to developers. Using Apache Spark can give any business a boost and help foster its growth. It is sure that you will also have a bright future!
Rated 4.5/5 based on 19 customer reviews
8597
Apache Spark Pros and Cons

Apache Spark:  The New ‘King’ of Big DataApac... Read More

Fundamentals of Apache Spark

IntroductionBefore getting into the fundamentals of Apache Spark, let’s understand What really is ‘Apache Spark’ is? Following is the authentic one-liner definition.Apache Spark is a fast and general-purpose, cluster computing system.One would find multiple definitions when you search the term Apache Spark. All of those give similar gist, just different words. Let’s understand these special keywords which describe Apache Spark. Fast: As spark uses in-memory computing it’s fast. It can run queries 100x faster. We will get to details of architecture later to understand this aspect better little later in the article. One would find the keywords ‘Fast’ and/or ‘In-memory’ in all the definitions. General Purpose: Apache spark is a unified framework. It provides one execution model for all tasks and hence very easy for developers to learn and they can work with multiple APIs easily. Spark offers over 80 high-level operators that make it easy to build parallel apps and one can use it interactively from the Scala, Python, R, and SQL shells.Spark powers a stack of libraries including SQL and DataFrames, MLlib for machine learning, GraphX, and Spark Streaming. You can combine these libraries seamlessly in the same application.Cluster Computing: Efficient processing of data on Set of computers (Refer commodity hardware here) or distributed systems. It’s also called a Parallel Data processing Engine in a few definitions. Spark is utilized for Big data analytics and related processing. One more important keyword associated with Spark is Open Source. It was open-sourced in 2010 under a   BSD license.Spark (and its RDD) was developed(earliest version as it’s seen today), in 2012, in response to limitations in the   MapReduce cluster computing paradigm. Spark is commonly seen as an in-memory replacement of MapReduce.Since its release, Apache Spark has seen rapid adoption due to its characteristics briefly discussed above.Who should go for Apache SparkBefore trying to find out whether Apache spark is for me? Or whether I have the right skill set, It's important to focus on the generality characteristic in further depth.Apache Spark consists of Spark Core and a set of libraries. The core is the distributed execution engine and the Java, Scala, and Python APIs offer a platform for distributed ETL application development. Additional libraries, built atop the core, allow diverse workloads for streaming, SQL, and machine learning.As Spark provides these multiple components, it’s evident that Spark is developed and widely utilized for big data and analytics.  Professionals who should learn Apache SparkIf one is aspiring to be landed into the following professions or anyone who has an interest in data and insights, Knowledge of spark will prove useful:Data ScientistsData EngineersPrerequisites of learning Apache SparkMost of the students looking for big data training, Apache spark is number one framework in big data. So most of the knowledge seekers looking for spark training, it is important to note that there are few prerequisites to learn apache spark.Before getting into Big data, you must have minimum knowledge on:Anyone of the programming languages >> Core   Python or Scala.Spark installations can be done on any platform but its framework is similar to Hadoop and hence having knowledge of HDFS and YARN is highly recommended. Having knowledge of Hive is an added advantage but is not mandatory.Basic knowledge of SQL. In SQL mainly select * from, joins and group by these three commands highly recommended.Optionally, knowing any cloud technology like AWS. Recommended for those who want to work with production-like environments.System requirements of Apache SparkOfficial site for  Apache Spark gives following recommendation (Traverse link for further details)Storage System: There are few ways to set this up as follows: Spark can run on the same node as HDFS. Spark standalone node cluster can be installed on the same nodes and configure Spark and Hadoop memory and CPU usage accordingly to avoid any interference.Or,1. Hadoop and Spark can execute on common Resource Manager ( Ex. Yarn etc)Or,2. Spark will be executing in same Local Area Network as HDFS but on separate nodes.Or3. If a requirement is a quick response and low latency from data stores then execute compute jobs on separate nodes than that of storage nodes.Local Disks: Typically 4-8 disks per node, configured without RAID.If underline OS is Linux then mount the disk with noatime option and in Spark environment configure spark.local.dir variable to be a comma-separated list of local disks.Note: For HDFS, it can be the same disk as HDFS.Memory: Minimum 8GB - 100s of GBs of memory per machine.A recommendation is the allocation of 75% of the memory to Spark.Network: 10GB or faster speed network.CPU cores: 8-16 Cores per machineHowever, for Training and Learning purpose and just to taste Spark, following are two available options: Run it locally Use AWS EMR (Or any cloud computing service)For learning purposes, minimum 4gb ram system with minimum 30gb disk may prove enough.History of Apache SparkSpark was primarily developed to Overcome the Limitations of MapReduce.Versioning: Spark initial version was version 0, version 1.6 is assumed to be a stable version and is being used in multiple commercial corporate projects. Version 2.3 is the latest available version. MapReduce is cluster computing  paradigm, which forces a particular linear  data flow structure on distributed programs: MapReduce programs read input data from disk,  map a function across the data,  reduce the results of the map, and store reduction results on disk. Due to multiple copies of data and multiple I/O as described, MapReduce takes lots of time to process the volume of data. MapReduce can do only batch time processing and is unsuitable for real-time data processingIt is unsuitable for trivial join like transformations. It’s unfit for large data on a network and also with OLTP data.Also, not suitable for graphics and interactive data.Spark overcomes all these limitations and able to do faster processing too on the local disk as well.Why Apache Spark?Numerous advantages of Spark have made its a market favorite.Let’s discuss one by one.Speed: Extends MapReduce Model to support computations like stream processing and interactive queries.Single Combination for processes and multiple tools:  Covers multiple workloads ( in a traditional system, it used to require different distributed systems), which makes combining different processing types and ease of tool management.Unification: Developers have to learn only one platform unlike multiple languages and tools in a traditional system.Support to different Resource Managers: Spark supports Hadoop HDFS system, and YARN for resource management but it’s not the only resource manager it supports. It works on MESOS and on any standalone scheduler like spark resource manager.Support for cutting-edge Innovation: Spark provides capabilities and support for an array of new-age technologies ranging from built-in machine learning libraries,   visualization tools, support for near processing (which was in a way the biggest challenge pre- spark era) and supports seamless integration with other deep learning frameworks like TensorFlow. This enables Spark to provide an innovative solution for new age use-cases.Spark can access diverse data sources and make sense of them all and hence it’s trending in the market over any other cluster computing software available. Who uses Apache SparkListing a few use cases of Apache spark below :1. Analytics - Spark can be very useful when building real-time analytics from a stream of incoming data.2. E-commerce - Information about the real-time transaction can be passed to streaming clustering algorithms like alternating least squares or K-means clustering algorithm. The results can be combined with data from other sources like social media profiles, product reviews on forums, customer comments, etc. to enhance the recommendations to customers based on new trends.Shopify: At Shopify, we underwrite credit card transactions, exposing us to the risk of losing money. We need to respond to risky events as they happen, and a traditional ETL pipeline just isn’t fast enough. Spark Streaming is an incredibly powerful real-time data processing framework based on Apache Spark. It allows you to process real-time streams like Apache Kafka using Python with incredible simplicity.Alibaba: Alibaba Taobao operates one of the world’s largest e-commerce platforms. We collect hundreds of petabytes of data on this platform and use Apache Spark to analyze these enormous amounts of data.3. Healthcare Industry –Healthcare has multiple use-cases of unstructured data to be processed in real-time. It has data ranging from image formats like scans etc to specific medical industry standards and wearable tracking devices. Many healthcare providers are keen on using spark for patient’s records to build 360 degrees view of the patient to do accurate diagnosis.MyFitnessPal: MyFitnessPal needed to deliver a new feature called “Verified Foods.” The feature demanded a faster pipeline to execute a number of highly sophisticated algorithms. Their legacy non-distributed Java-based data pipeline was slow, did not scale, and lacked flexibility.Here are a few other examples from industry leaders:Regeneron: Future of Drug Discovery with Genomics at Scale powered by SparkZeiss: Using Spark Structured Streaming for Predictive MaintenanceDevon Energy: Scaling Geographic Analytics with Spark GraphXYou can also learn more about use cases of Apache Spark  here.Career Benefits:Career Benefits of Spark for you as an individual:Apache Spark developers earn the highest average salary among all other programmers. According to its  2015 Data Science Salary Survey, O’Reilly found strong correlations between those who used Apache Spark and those who were paid more money. In one of its models, using Spark added more than $11,000 to the median salary.If you’re considering switching to this extremely in-demand career then taking up the  Apache Spark training will be an added advantage. Learning Spark will give you a steep competitive edge and can land you up in market best-paying jobs with top companies. Spark has gained enough adherents over the years to place it high on the list of fastest-growing skills; data scientists and sysadmins have evaluated the technology and clearly seen what they liked.  April’s Dice Report explored the fastest-growing technology skills, based on an analysis of job postings and data from Dice’s annual salary survey. The results are below; percentages are based on year-over-year growth in job postings:Benefits of Spark implementing Spark in your organization:Apache spark is now a decade older but still going strong. Due to lightning-fast processing and numerous other advantages discussed so far, Spark is still the first choice of many organizations.Spark is considered to be the most popular open-source project on the planet, with more than 1,000 contributors from 250-plus organizations, according to Databricks.ConclusionTo sum up, Spark helps to simplify the computationally intensive task of processing high volumes of real-time or batch data. It can seamlessly integrate with complex capabilities such as machine learning and graph algorithms. In short, Spark brings exclusive Big Data processing (which earlier was only for giant companies like Google) to the masses.Do let us know how your learning experience was, through comments below.Happy Learning!!!
Rated 4.5/5 based on 13 customer reviews
9785
Fundamentals of Apache Spark

IntroductionBefore getting into the fundamentals o... Read More

How Big Data Can Help You Understand Your Customers and Grow Your Business

What’s the main purpose of a marketing campaign for any business? You’re trying to convince the customers you offer exactly what they need. What do you do to get there? You find out what they need. This is where big data gets into the picture. Big data is a general term for all information that allows you to understand the purchasing decisions of your target consumers That’s not all. Big data also helps you create a sustainable budget, find the best way to manage your business, beat the competition, and create higher revenue. In essence, big data is all information that helps you grow your brand. The process of analyzing and successfully using that data is called big data analytics. Now that we got the definition out of the way, let’s get practical. We’ll help you realize how you can use big data to understand the behavior of your customers and grow your brand. Where Can You Find Big Data? This is the big question about big data: where do you find it? When you’re looking for data that you could immediately turn into useful information, you should start with the historical data of your business. This includes all information for your business you collected since it was formed. The earnings, revenues, stock price action… everything you have. That data is already available to you. You can use it to understand how your business worked under different circumstances. The US Census Bureau holds an enormous amount of data regarding US citizens. You can use the information about the population economy, and products to understand the behavior of your target consumers. gov is another great website to explore. It gives you data related to consumers, ecosystems, education, finance, energy, public safety, health, agriculture, manufacturing, and few other categories. Explore the field relevant to your business and you’ll find data you can use. This information is for US citizens. If you need a similar tool for the EU, you can explore the European Union Open Data Portal. Facebook’s Graph API gives you a huge amount of information about the users of the platform. How to Use Big Data to Your Brand’s Advantage Collecting big data is not that hard. Information is everywhere. However, the huge volume of information you collect might confuse you. For now, you might want to focus on the historical data for your business. That should be enough for you to understand the behavior of your customers. When you understand how the analytics work, you can start comparing your historical data with the information you get from governmental and social media sources. These are the main questions to ask when analyzing big data: The average amount your customers spend on a typical purchase. This information helps you understand their budget and spending habits. Did they spend more money on an average purchase when they used promotions? What’s the situation with conversion? How many of the social media followers follow a link and become actual customers? These rates help you determine the effect of your marketing campaign. When you understand it, you’ll be able to improve it. How many new customers did you attract through promotions? Did those activities help you increase the awareness for your brand? How much have you spent on marketing and sales to attract a single customer? Divide the total amount of expenses for promotional activities with the number of customers you attracted while the campaign lasted. You’ll get the acquisition cost of a single customer. If it’s too large, you’ll need to restructure your promotional activities. Compare historical data to identify the campaigns that were most and least successful in this aspect. What do your customers require in order to stay loyal to your brand? Do they ask for more support or communication? How satisfied are your customers with the products or services you offer? What’s the difference between the categories of happy and unhappy customers? When you determine the factors that make your customers happy, you’ll be able to expand on them. When you identify the things that lead to dissatisfaction, you’ll work on them. Every Business Benefits from Big Data You feel like you have to own a huge business to get interested about big data? That’s a misconception. It doesn’t matter how big your company is. You still have tons of data to analyze, and you can definitely benefit from it & bigdata solve problems easily Collect all data described above and compare it with the way your customers behaved in the past. Are you growing? If yes, why? If not, why? The key to understanding the behavior of your customers is to give this information a human face. Connect the numbers with the habits and spending behavior of your real customers. When you relate the data to actual human experience, you’ll be able to develop customers personas. You’ll increase the level of satisfaction your consumers get. When you do that, the growth of your business will be inevitable.
Rated 4.0/5 based on 20 customer reviews
How Big Data Can Help You Understand Your Customer...

What’s the main purpose of a marketing campaign ... Read More

Useful links