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How Big Data Can Solve Enterprise Problems

Many professionals in the digital world have become familiar with the hype cycle. A new technology enters the tech world amid great expectations. Undoubtedly, dismay sets in and retrenchment stage starts, practice and process catch up to assumptions and the new value is untied. Currently, there is apparently no topic more hyped than big data and there is already no deficit of self-proclaimed pundits. Yet nearly 55% of big data projects fail and there is an increasing divide between enterprises that are benefiting from its use and those who are not. However, qualified data scientists, great integration across departments, and the ability to manage expectations all play a part in making big data work for your organization. It is often said that an organization’s future is dependent on the decisions it takes. Since most of the business decisions are backed by data available at hand. The accurate the information, the better they are for the business. Gone are the days when data was only used as an aid in better decision making. But now, with big data, it has actually become a part of all business decisions. For quite some time now, big data has been changing the way business operations are managed, how they collect data and turn it into useful and accurate information in real-time. Today, let’s take a look at solving real-life enterprise problems with big data. Predictive Analysis Let’s assume that you have a solid knowledge of the emerging trends and technologies in the market or when your infrastructure needs a good maintenance. With huge amounts of data, you can easily predict trends and your future needs for the business. This sort of knowledge gives you an edge over your peers in this competitive world. Enhancing Market Research Regardless of the business vertical, market research is an essential part of business operations. With the ever-changing needs and aspirations of your customers, businesses need to find ways to get into the mind of customers with better and improved products and services. In such scenarios, having large volumes of data in hand will let you carry out detailed market research and thus enhancing your products and services. Streamlining Business Process For any enterprise, streamlining the business process is a crucial link to keeping the business sustainable and lucrative. Some effective modifications here and there can benefit you in the long run by cutting down the operational costs. Big data can be utilized to overhaul your whole business process right from raw material procurement to maintaining the supply chain. Data Access Centralization It is an inevitable fact that the decentralized data has its own advantages and one of the main restrictions arises from the fact that it can build data silos. Large enterprises with global presence frequently encounter such challenges. Centralizing conventional data often posed a challenge and blocked the complete enterprise from working as one team. But big data has entirely solved this problem, offering visibility of the data throughout the organization. How are you navigating the implications of all that data within your enterprise? Have you deployed big data in your enterprise and solved real-life enterprise problems? Then we would love to know your experiences. Do let us by commenting in the section below.
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How Big Data Can Solve Enterprise Problems

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How Big Data Can Solve Enterprise Problems

Many professionals in the digital world have become familiar with the hype cycle. A new technology enters the tech world amid great expectations. Undoubtedly, dismay sets in and retrenchment stage starts, practice and process catch up to assumptions and the new value is untied.

Currently, there is apparently no topic more hyped than big data and there is already no deficit of self-proclaimed pundits. Yet nearly 55% of big data projects fail and there is an increasing divide between enterprises that are benefiting from its use and those who are not. However, qualified data scientists, great integration across departments, and the ability to manage expectations all play a part in making big data work for your organization.

It is often said that an organization’s future is dependent on the decisions it takes. Since most of the business decisions are backed by data available at hand. The accurate the information, the better they are for the business. Gone are the days when data was only used as an aid in better decision making. But now, with big data, it has actually become a part of all business decisions. For quite some time now, big data has been changing the way business operations are managed, how they collect data and turn it into useful and accurate information in real-time. Today, let’s take a look at solving real-life enterprise problems with big data.

Predictive Analysis

Let’s assume that you have a solid knowledge of the emerging trends and technologies in the market or when your infrastructure needs a good maintenance. With huge amounts of data, you can easily predict trends and your future needs for the business. This sort of knowledge gives you an edge over your peers in this competitive world.

Enhancing Market Research

Regardless of the business vertical, market research is an essential part of business operations. With the ever-changing needs and aspirations of your customers, businesses need to find ways to get into the mind of customers with better and improved products and services. In such scenarios, having large volumes of data in hand will let you carry out detailed market research and thus enhancing your products and services.

Streamlining Business Process

For any enterprise, streamlining the business process is a crucial link to keeping the business sustainable and lucrative. Some effective modifications here and there can benefit you in the long run by cutting down the operational costs. Big data can be utilized to overhaul your whole business process right from raw material procurement to maintaining the supply chain.

Data Access Centralization

It is an inevitable fact that the decentralized data has its own advantages and one of the main restrictions arises from the fact that it can build data silos. Large enterprises with global presence frequently encounter such challenges. Centralizing conventional data often posed a challenge and blocked the complete enterprise from working as one team. But big data has entirely solved this problem, offering visibility of the data throughout the organization.

How are you navigating the implications of all that data within your enterprise? Have you deployed big data in your enterprise and solved real-life enterprise problems? Then we would love to know your experiences. Do let us by commenting in the section below.

Shahid

Shahid Mansuri

Blog Author

Shahid Mansuri co-founded Peerbits, one of the leading mobile app development companies India, in 2011. His visionary leadership and flamboyant management style have yield fruitful results for the company. He believes in sharing his strong knowledge base with leaned concentration on entrepreneurship and business. Being an avid nature lover, he likes to flaunt his pajamas on beach during the vacations.
 


Website : https://www.peerbits.com

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Tony

Hi, Thanks for sharing the information. These information will really help us a lot.

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5 Best Data Processing Frameworks

“Big data” is a phrase that was coined to refer to amounts of datasets that are so large, traditional data processing software simply can’t manage them. For example, big data is used to pick out trends in economics, and those trends and patterns are used to predict what will happen in the future. These vast amounts of data require more robust computer software for processing, best handled by data processing frameworks. These are the top preferred data processing frameworks, suitable for meeting a variety of different needs of businesses. Hadoop This is an open-source batch processing framework that can be used for the distributed storage and processing of big data sets. Hadoop relies on computer clusters and modules that have been designed with the assumption that hardware will inevitably fail, and those failures should be automatically handled by the framework. There are four main modules within Hadoop. Hadoop Common is where the libraries and utilities needed by other Hadoop modules reside. The Hadoop Distributed File System (HDFS) is the distributed file system that stores the data. Hadoop YARN (Yet Another Resource Negotiator) is the resource management platform that manages the computing resources in clusters, and handles the scheduling of users’ applications. The Hadoop MapReduce involves the implementation of the MapReduce programming model for large-scale data processing. Hadoop operates by splitting files into large blocks of data and then distributing those datasets across the nodes in a cluster. It then transfers code into the nodes, for processing data in parallel. The idea of data locality, meaning that tasks are performed on the node that stores the data, allows the datasets to be processed more efficiently and more quickly. Hadoop can be used within a traditional onsite datacenter, as well as through the cloud. Apache Spark Apache Spark is a batch processing framework that has the capability of stream processing, as well, making it a hybrid framework. Spark is most notably easy to use, and it’s easy to write applications in Java, Scala, Python, and R. This open-source cluster-computing framework is ideal for machine-learning, but does require a cluster manager and a distributed storage system. Spark can be run on a single machine, with one executor for every CPU core. It can be used as a standalone framework, and you can also use it in conjunction with Hadoop or Apache Mesos, making it suitable for just about any business. Spark relies on a data structure known as the Resilient Distributed Dataset (RDD). This is a read-only multiset of data items that is distributed over the entire cluster of machines. RDDs operate as the working set for distributed programs, offering a restricted form of distributed shared memory. Spark is capable of accessing data sources like HDFS, Cassandra, HBase, and S3, for distributed storage. It also supports a pseudo-distributed local mode that can be used for development or testing. The foundation of Spark is Spark Core, which relies on the RDD-oriented functional style of programming to dispatch tasks, schedule, and handle basic I/O functionalities. Two restricted forms of shared variables are used: broadcast variables, which reference read-only data that has to be available for all the nodes, and accumulators, which can be used to program reductions. Other elements included in Spark Core are: Spark SQL, which provides domain-specific language used to manipulate DataFrames. Spark Streaming, which uses data in mini-batches for RDD transformations, allowing the same set of application code that is created for batch analytics to also be used for streaming analytics. Spark MLlib, a machine-learning library that makes the large-scale machine learning pipelines simpler. GraphX, which is the distributed graph processing framework at the top of Apache Spark. Apache Storm This is another open-source framework, but one that provides distributed, real-time stream processing. Storm is mostly written in Clojure, and can be used with any programming language. The application is designed as a topology, with the shape of a Directed Acyclic Graph (DAG). Spouts and bolts act as the vertices of the graph. The idea behind Storm is to define small, discrete operations, and then compose those operations into a topology, which acts as a pipeline to transform data. Within Storm, streams are defined as unbounded data that continuously arrives at the system. Sprouts are sources of data streams that are at the edge of the topology, while bolts represent the processing aspect, applying an operation to those data streams. The streams on the edges of the graph direct data from one node to another. These bolts and sprouts define sources of information and allow batch, distributed processing of streaming data, in real-time. Samza Samza is another open-source framework that offers near a real-time, asynchronous framework for distributed stream processing. More specifically, Samza handles immutable streams, meaning transformations create new streams that will be consumed by other components without any effect on the initial stream. This framework works in conjunction with other frameworks, using Apache Kafka for messaging and Hadoop YARN for fault tolerance, security, and management of resources. Samza uses the semantics of Kafka to define how it handles streams. Topic refers to each stream of data that enters a Kafka system. Brokers are the individual nodes that are combined to make a Kafka cluster. A producer is any component that writes to a Kafka topic, and a consumer is any component that reads from a Kafka topic. Partitions are used to divide incoming messages in order to distribute a topic among the different nodes. Flink Flink is a hybrid framework, open-source, and stream processes, but can also manage batch tasks. It uses a high-throughput, low-latency streaming engine that is written in Java and Scala, and the runtime system that is pipelined allows for the execution of both batch and stream processing programs. The runtime also supports the execution of iterative algorithms natively. Flink’s applications are all fault-tolerant and can support exactly-once semantics. Programs can be written in Java, Scala, Python, and SQL, and Flink offers support for event-time processing and state management. The components of the stream processing model in Flink include streams, operators, sources, and sinks. Streams are immutable, unbounded datasets that go through the system. Operators are functions that are used on data streams to create other streams. Sources are the entry points for streams that enter into the system. Sinks are places where streams flow out of the Flink system, either into a database or into a connection to another system. Flink’s batch processing system is really just an extension of the stream processing model. Flink does not provide its own storage system, however, so that means you will have to use it in conjunction with another framework. That should not be a problem, as Flink is able to work with many other frameworks. Data processing frameworks are not intended to be one-size-fits-all solutions for businesses. Hadoop was originally designed for massive scalability, while Spark is better with machine learning and stream processing. A good IT services consultant can evaluate your needs and offer advice. What works for one business may not work for another, and to get the best possible results, you may find that it’s a good idea to use different frameworks for different parts of your data processing.
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5 Best Data Processing Frameworks

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Who Can Do Hadoop course?

Hadoop is a Java-based programming framework which is open source. This framework is useful in processing and storage of huge data sets in a distributed computing environment. It handles thousands of terabytes of data and runs applications on systems with thousands of hardware nodes. The data transfer rates among the nodes are very high and even allows the system to continue working in case a node fails. Hadoop admin training lets you understand the Hadoop framework, HDFS and every related technology. It has four industry-based projects and is suited to data engineers, IT professionals, cloud administrators and system administrators. There will be different pros & cons in hadoop and more details about the course are given towards the end of the article. Who is eligible to do Hadoop course? Planning to learn Hadoop is a good decision if you are working in the Information Technology industry. There are no specific prerequisites to start learning the framework. But, it is recommended to know the basics of Java and Linux if you want to become a Hadoop expert and opt for a career in it. What if you don’t have much knowledge about Java and Linux? Experts say that you can still learn Hadoop course. While learning the framework, you can side by side learn Java and Linux for a few hours every day. Role of Java Though Java is not an exact prerequisite for Hadoop, knowing the basics is an added advantage. But, tools like Pig and Hive built on Hadoop have their own high-level languages to work with data. Writing code can be done in any language like Python, C, Ruby, Perl etc. These languages enable reading from the standard input and writing to the standard output with Hadoop streaming. In addition, the frameworks like Pig and Hive provide high level abstractions which do not require you to know Java. While Hive is programmed using HiveQL, Pig can be programmed in Pig Latin.  Both these are automatically converted to MapReduce programs in Java. Benefits of knowing Java Though you can use streaming to write the map and reduce the number of functions in the language you choose, there exists some advanced features that are only available in Java API at present. At times, you may have to check the Hadoop code in detail to find out why something is behaving like it is or to get more idea about the functionality of a particular module. This is again one situation when Java becomes useful. Many Hadoop projects come with roles like developer, tester, network administrator etc. some of which need Java knowledge while some don’t. If you are keen to dive into Hadoop administration then you can do so without any hesitation even if you don’t know Java. In course of time, you can improve your knowledge of Java so that you can be an expert in Hadoop. Role of Linux As you must be aware, Hadoop was initially built on Linux though it now runs fine in Windows. So, Linux is preferred for installing and managing Hadoop. Having a good foundation in Linux will help you grasp Hadoop better especially, the HDFS command line parameters. Role of C/C++ If you know to program in languages like C or C++, it would be easy for you to learn Java as they are not too different. So, learn some Java with your C/C++ knowledge and then move on to Hadoop training. You can also straight away start learning Hadoop with the C/C++ knowledge. There are many parts of the Hadoop system written in C and C++. If you can write map-reduce applications in C++ then Hadoop has pipes which is the library that permits C++ source code to be used for Reducer and Mapper code. Hadoop administrator certification Hadoop administration training prepares you for Cloudera’s CCAH certification and helps you build all the skills required for Big Data admin assignments. This specific course covers the Core Hadoop distributions which includes Apache Hadoop and Vendor specific distribution. You will learn about Cloudera manager and its capabilities along with the need for cluster management solutions. You will understand how to set up Hadoop cluster and its components like Pig, Hive, Sqoop etc. While taking the Hadoop admin certification, you will come to know about Hadoop’s Distributed File System and its computation/processing framework. You will also be taught to plan, secure, monitor and safeguard a cluster. Thus, the course will build a solid base in the advanced concepts of Big Data and the related technologies of Hadoop stack and the components in the Hadoop Ecosystem. After the completion of the course, you will have basic knowledge of Master Big Data with hadoop certification and its characteristics and the options used by companies to manage Big Data. You will understand the architecture of Hadoop framework and its distributed file system. Thus, learning Hadoop is one of the best options to go up in the admin career.
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