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

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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?

  1. 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.
  2. 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.
  3. 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.
  4. 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.

Robert

Robert Morris

Blog Author

Robert Morris is a freelance writer. He writes articles on business, career, marketing, technology and currently is working as a blog editor at the educational blog <a href="http://askpetersen.com" />askpetersen.com</a>.

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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!
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Apache Spark Pros and Cons

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Apache Spark Vs Hadoop - Head to Head Comparison

Over the past few years, data science has been one of the most sought-after multidisciplinary fields in the world today. It has established itself as an essential component of numerous industries such as marketing optimisation, risk management, marketing analytics. fraud detection, agriculture, etc. Understandably, this has lead to increasing demand for resorting to different approaches to data.When we talk about Apache Spark and Hadoop, it is really difficult to compare them with each other. We should be aware that both possess important features in the world of data science and big data. Hadoop excels over Apache Spark in some business applications, but when processing speed and ease of use is taken into account, Apache Spark has its own advantages that make it unique. The most important thing to note is, neither of these two can replace each other. However, since they are compatible with each other, they can be used together to produce very effective results for many big data applications.To analyse how important these two platforms are, there is a set of parameters with which we can discuss their efficiencies such as performance, ease of use, cost, data processing, compatibility, fault tolerance, scalability, and security. In this article, we will talk about Apache Spark and Hadoop individually for a bit, followed by stressing these parameters to better understand their significance in data science and big data.What is Hadoop?Hadoop, also known as Apache Hadoop, is a project formed by Apache.org that includes a software library and a framework that enables the usage of simple programming models to distributed processing of large data sets (big data) across computer clusters. Hadoop is quite efficient in scaling up from single computer systems to a lot of commodity system, offering substantial local storage. Due to this, Hadoop is considered as an omnipresent heavyweight in the big data analytics space. There are modules that work together to form the Hadoop framework. Here are the main Hadoop framework modules:Hadoop CommonHadoop Distributed File System (HDFS)Hadoop YARNHadoop MapReduceHadoop’s core is based on the above four modules followed by many others like Ambari, Avro, Cassandra, Hive, Pig, Oozie, Flume, and Sqoop. These are responsible for improving and extending Hadoop’s power to big data applications and large data set processing.Hadoop is utilised by numerous companies using big data sets and analytics and is the de facto model for big data applications. Initially, it was designed to take care of crawling and searching billions of web pages and collecting their information into a database, This resulted in Hadoop Distributed File System (HDFS), a distributed file system designed to run on commodity hardware and Hadoop MapReduce, a processing technique and a program model for distributed computing based on java.Hadoop comes handy when companies find data sets too large and complex to not being able to process the information in reasonably sufficient time. Since crawling and searching the web are text-based tasks, Hadoop MapReduce comes in handy as it is an exceptional text processing engine.An Overview of Apache SparkAn open-source distributed general-purpose cluster-computing framework, Apache Spark is considered as a fast and general engine for large-scale data processing. Compared to heavyweight Hadoop’s Big Data framework, Spark is very lightweight and faster by nearly 100 times. Although the facts say so, in fact, Spark runs up to 10 times faster on disk. Apart from that, it can perform batch processing but it really is good at streaming workloads, interactive queries, and machine-based learning.✓Streaming workloads✓Interactive queries✓Machine-based learning.Spark engine’s real-time data processing capability has a clear edge over Hadoop MapReduce’s disk-bound, batch processing one. Not only is Spark compatible with Hadoop and its modules, but it is also listed as a module on Hadoop’s project page. And because Spark can run in Hadoop clusters through YARN (Yet Another Resource Negotiator), it has its own page and a standalone mode. It can run as a Hadoop module and as a standalone solution which makes it difficult to make direct comparisons.Despite these facts, Spark is expected to diverge and might even replace Hadoop, especially in terms of faster access to processed data. Spark’s cluster computing feature enables it to compete with only Hadoop MapReduce and not the entire Hadoop ecosystem. That is why it can use HDFS despite not having its own distributed file system. To be concise, Hadoop MapReduce uses persistent storage whereas Spark uses Resilient Distributed Datasets (RDDs). What is RDD? This will be stressed in the Fault Tolerance section.The differences between Apache Spark and HadoopLet us have a look at the parameters using which we can compare the features of Apache Spark with Hadoop.Apache Spark vs Hadoop in a nutshellApache SparkParametersHadoopProcesses everything in memoryPerformance-wiseHadoop MapReduce uses batch processingHas user-friendly APIs for multiple programming languagesEase of UseHas add-ons such as Hive and PigSpark systems cost moreCostsHadoop MapReduce systems cost lesserShares every Hadoop MapReduce compatibilityCompatibilityCompliments Apache Spark seamlesslyHas GraphX, its own graph computation libraryData ProcessingHadoop MapReduce operates in sequential stepsSpark uses Resilient Distributed Datasets (RDDs)Fault ToleranceUtilises TaskTrackers to keep the JobTracker tickingComparatively lesser scalabilityScalabilityLarge ScalabilityProvides authentication via shared secret (password authentication)SecuritySupports Kerberos authenticationPerformance-wiseSpark is definitely faster when compared to Hadoop MapReduce. However, they cannot be compared because they perform processing in different styles. Spark is way faster because it processes everything in memory, even using disk for data that does not all fit into memory. The in-memory processing of Spark performs near real-time analytics for data from machine learning, log monitoring, marketing campaigns, Internet of Things sensors, security analytics, and social media sites. Hadoop MapReduce, on the other hand, utilises the batch-processing method so it understandably was never created for mesmerising speed. As a matter of fact, it was initially created to continuously gather information from websites during the times when data in or near real-time were not required.Ease of UseSpark does not only have a good reputation for its excellent performance, but it is also relatively easy to use along with providing additional support for languages like user-friendly APIs for Scala, Java, Python, and Spark SQL. Since Spark SQL is quite comparable to SQL 92, the user requires no additional knowledge to use it.Supported Languages:APIs for ScalaJavaPythonSpark SQL.Additionally, Spark is armed with an interactive mode to allow developers and users get instant feedback for questions and other actions. Hadoop MapReduce makes up for the lack of any interactive mode with add-ons like Hive and Pig, thus easing the workflow of Hadoop MapReduce.CostsApache Spark and Apache Hadoop MapReduce are both free open-source software.However, because Hadoop MapReduce’s processing is disk-based, it utilises standard volumes of memory. This results in companies buying faster disks with a lot of disk space to run Hadoop MapReduce. In stark contrast to this, Spark requires a lot of memory but compensates by settling with a standard amount of disk space running at standard speeds.Apache Spark and Apache Hadoop CompatibilityBoth Spark and Hadoop MapReduce are compatible with each other. Moreover, Spark shares every Hadoop MapReduce compatibility for data sources, file formats, and business intelligence tools via JDBC and ODBC.Data ProcessingHadoop MapReduce is a batch-processing engine. So how does it work? Well, it works in sequential steps.Step 1: Reads data from the clusterStep 2: Performs its operation on the dataStep 3: Writes the results back to the clusterStep 4: Reads updated data from the clusterStep 5: Performs the next data operationStep 6: Writes those results back to the clusterStep 7: Repeat.Spark performs in a similar manner, but the process doesn’t go on. It includes a single step and then to memory.Step 1: Reads data from the clusterStep 2: Performs its operation on the dataStep 3: Writes it back to the cluster.Moreover, Spark has GraphX, its own graph computation library. GraphX presents the same data as graphs and collections. Users have the option to use Resilient Distributed Datasets (RDDs) to transform and join graphs. This will be further addressed below in the Fault Tolerance section.Fault ToleranceThere are two different ways in which Hadoop MapReduce and Spark resolve the fault tolerance issue. Hadoop MapReduce utilises nodes like TaskTrackers to keep the JobTracker ticking. On the process being interrupted, the JobTracker reassigns every pending and in-progress operation to another TaskTracker. Although this process effectively provides fault tolerance, the completion times might get majorly affected even for operations having just a single failure.Spark, in this case, applies Resilient Distributed Datasets (RDDs), fault-tolerant collections of elements that can be operated side by side. References can be provided by RDDs in the form of datasets in an external storage system like shared filesystems, HDFS, HBase, or whatever available data source. This results in allowing a Hadoop InputFormat and Spark can create RDDs from every storage source that is backed by Hadoop. That covers local filesystems or one of those listed earlier.Below-mentioned is five main properties that an RDD possesses:A list of partitionsA function for computing each splitA list of dependencies on other RDDsA Partitioner for key-value RDDs by choice (provided that the RDD is hash-partitioned)Optionally, a list of preferred locations to compute each split on (e.g. block locations for an HDFS file)The persistence of RDDs to cache a dataset in memory across operations enables the speeding up of future actions by possibly ten folds. The cache of Spark is fault-tolerant, it will recomputed automatically by making use of the original transformations provided any partition of an RDD is lost.ScalabilityIn terms of scaling up, both Hadoop MapReduce and Spark are on equal terms in using the HDFS. Reports say that Yahoo holds a 42,000 node Hadoop cluster with no bounds while the most comprehensive Spark cluster holds 8,000 nodes. However, in order to support output expectations, the cluster sizes are expected to grow along with that of big data.SecurityKerberos authentication, considered to be quite hectic to manage is supported by Hadoop. Nevertheless, companies have been assisted by third-party vendors to leverage Active Directory Kerberos and LDAP for authentication and also allow data encrypt for in-flight and data at rest. Access control lists (ACLs) a traditional file permissions model are supported by Hadoop while it provides Service Level Authorization for user control in job submission, resulting in clients having the right permissions without any fail.For Spark though, it presently offers somewhat inadequate security as it provides authentication via shared secret (password authentication). However, if the user runs Spark on HDFS, then it can utilise HDFS ACLs and file-level permissions. Moreover, running Spark on YARN will enable the latter to have the capacity of using Kerberos authentication. That is the security takeaway from using Spark.  ConclusionApache Spark and Apache Hadoop form the perfect combination for business applications. Where Hadoop MapReduce has been a revelation in the big data market for businesses requiring huge datasets to be brought under control by commodity systems, Apache Spark’s speed and comparative ease of use compliments the low-cost operation involving Hadoop MapReduce.Like we discussed at the beginning of this article that neither of these two can replace one another, Spark and Hadoop form a lethal and effective symbiotic partnership. While Hadoop has features like a distributed file system that Spark does not have, the latter presents real-time, in-memory processing for the required data sets. Both Hadoop and Spark form the perfect combination for the ideal big data scenario. Rest assured, in this situation, both working in the same team is what goes in favour of big data professionals.You would be interested to know that Knowledgehut offers world-class training for Apache Spark and Hadoop. Feel free to check these courses to enhance your knowledge about both Apache Spark and Hadoop.
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Apache Spark Vs Hadoop - Head to Head Comparison

Over the past few years, data science has been one... Read More

Best ways to learn Apache Spark

If you ask any industry expert what language should you learn for Big Data? You will get an obvious reply to learn Apache Spark. Apache Spark is widely considered as the future of the Big Data industry. Since Apache Spark has stepped into Big data market, it has gained a lot of recognition for itself. Today, most of the cutting-edge companies like Apple, Facebook, Netflix, and Uber, etc. have deployed Spark at massive scale. In this blog post, we will understand why one should learn Apache Spark? And several ways to learn it. Apache Spark is a powerful open-source framework for the processing of large datasets. It is the most successful projects in the Apache software foundation. Apache Spark basically designed for fast computation, also which runs faster than Hadoop. Apache Spark can collectively process huge amount of data present in clusters over multiple nodes. The main feature of Apache Spark is its in-memory cluster computing that increases the processing speed of an application.Why You Should Learn Apache SparkApache Spark has become the most popular unified analytics engine for Big Data and Machine Learning. Enterprises are widely utilizing Spark which in turn is increasing demand for Apache Spark developers. Apache Spark developers are the ones earning the highest salary. IT professionals can leverage this upcoming skill set gap by pursuing a certification in Apache Spark. A developer with expertise in Apache Spark skills can earn an average salary of $78K as per Payscale. It is the right time for you to learn Apache Spark as there is a very high demand for Spark developers chances of getting a job is high.Here are the reasons why you should learn Apache Spark today:In order to go with the growing demand for Apache SparkTo fulfill the demands for Spark developersTo get benefits of existing big data investmentsResources to learn ReactTo learn Spark, you can refer to Spark’s website. There are multiple resources you will find to learn Apache Spark, from books, blogs, online videos, courses, tutorials, etc. With these multiple resources available today, you might be in the dilemma of choosing the best resource, especially in this fast-paced and swiftly evolving industry.BooksCertificationsVideosTutorials, Blogs, and TalksHands-on Exercises 1. BooksWhen was the last time you read a book? Do you have reading habits? If not, it’s the time to read the books. Reading has a significant number of benefits. Those aren’t fans of books might miss out the importance of Apache Spark. To learn Apache Spark, you can skim through the best Apache Spark books given below.Apache Spark in 24 hours is a perfect book for beginners which comprises 592 pages covering various topics. An excellent book to learn in a very short span of time. Apart from this, there are also books which will help you master.Here is the list of top books to learn Apache Spark:Learning Spark by Matei Zaharia, Patrick Wendell, Andy Konwinski, Holden KarauAdvanced Analytics with Spark by Sandy Ryza, Uri Laserson, Sean Owen and Josh WillsMastering Apache Spark by Mike FramptonSpark: The Definitive Guide – Big Data Processing Made SimpleSpark GraphX in ActionBig Data Analytics with SparkThese are the various Apache Spark books meant for you to learn. These books include for beginners and others for the advanced level professionals.2. Apache Spark Training and CertificationsOne more way to learn Apache Spark is through taking up training. Apache Spark Training will boost your knowledge and also help you learn from experience. You will be certified once you are done with training. Getting this certification will help you stand out of the crowd. You will also gain hands-on skills and knowledge in developing Spark applications through industry-based real-time projects.3. Videos:Videos are really good resources to help you learn Apache Spark. Following are the few videos will help you understand Apache Spark.Overview of SparkIntro to Spark - Brian ClapperAdvanced Spark Analytics - Sameer FarooquiSpark Summit VideosVideos from Spark Summit 2014, San Francisco, June 30 - July 2, 2013Full agenda with links to all videos and slidesTraining videos and slidesVideos from Spark Summit 2013, San Francisco, Dec 2-3-2013Full agenda with links to all videos and slidesYouTube playist of all KeynotesYouTube playist of Track A (Spark Applications)YouTube playist of Track B (Spark Deployment, Scheduling & Perf, Related projects)YouTube playist of the Training Day (i.e. the 2nd day of the summit)You can learn more on Apache Spark YouTube Channel for videos from Spark events. 4. Tutorials, Blogs, and TalksUsing Parquet and Scrooge with Spark — Scala-friendly Parquet and Avro usage tutorial from Ooyala's Evan ChanUsing Spark with MongoDB — by Sampo Niskanen from WellmoSpark Summit 2013 — contained 30 talks about Spark use cases, available as slides and videosA Powerful Big Data Trio: Spark, Parquet and Avro — Using Parquet in Spark by Matt MassieReal-time Analytics with Cassandra, Spark, and Shark — Presentation by Evan Chan from Ooyala at 2013 Cassandra SummitRun Spark and Shark on Amazon Elastic MapReduce — Article by Amazon Elastic MapReduce team member Parviz DeyhimSpark, an alternative for fast data analytics — IBM Developer Works article by M. Tim Jones 5. Hands-on ExercisesHands-on exercises from Spark Summit 2014 - These exercises will guide you to install Spark on your laptop and learn basic concepts.Hands-on exercises from Spark Summit 2013 - These exercises will help you launch a small EC2 cluster, load a dataset, and query it with Spark, Spark Streaming, and MLlib.So these were the best resources to learn Apache Spark. Hope you found what you were looking for. Wish you a Happy Learning!
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Best ways to learn Apache Spark

If you ask any industry expert what language shoul... Read More

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