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4 Types Of Data Analytics To Improve Decision-Making

If you are on CSE stack portal, there’s a good chance that you are already well acquainted with the general terms like ‘Data Analytics’, ‘Big Data’ and ‘Business Intelligence’ lead to different things in different circumstances. But have you thought what would be the right BI platform to hack through a wide number of solutions for business success? In this article, I will knuckle down disambiguating the term ‘Data Analytics’ by splitting it down into 4 different types and aligning them with decision-making objectives. Descriptive Analytics: What happened? The commonest of the common type of Analytics, Descriptive Analytics offers the analyst a comprehensive view of key metrics and measures within an organization. It analyses the data available in real-time as well as historical data to derive meaningful insights regarding the future of a company. The main aim of this basic type of analytics is to discover the reasons behind pretentious success or failure in the past, as a result it is also known as ‘Reporting Bedrock’. A business learns from its past behaviors, and draws inceptions based on those observations about its future outcomes, how they are going to affect. Descriptive Analytics is clouted the best when a business is on its way to understand the overall performance of the organization at an aggregate level and perceive the various aspects. The best example of this would be a profit and loss statement. In the same way, analysts can possess data on a huge population of customers – delving deeper into mastering the demographic information of these customers can be classified as ‘descriptive analytics’. Diagnostic Analytics: What made it happen? The next stop to understand the intricacies of Data Analytics after Descriptive Analytics is Diagnostic Analytics. After assessing descriptive data, brilliant diagnostic analytical tools enable an analyst to go deeper into the problem, with the help of drilldowns and queries to eradicate the root-cause of the trouble. In simple words, in this analytics, historical data are ascertained against other data to reveal the answer of the question ‘why it happened’. With Diagnostic Analytics, the companies are now able to make breakthroughs, to pick out the dependencies and to discern patterns. Organizations prefer this type of analytics as it gives them a deeper perception regarding a specific problem. On the other hand, the organizations should keep all the detailed information by their side, otherwise data collection may turn out to be time-consuming. Effectively designed, well-integrated Business Information (BI) dashboards that assimilate the readings of time-series data, and participating filters and drilldown capabilities are deemed perfect for such analysis. Predictive Analytics: What is going to happen? It is all in the right predictions. Predictive Analytics involve analysis of past data patterns and trends to accurately forecast the future business outcome. It helps in determining realistic goals for the company and its effective execution and moderating expectations, by manipulating the findings of Descriptive and Diagnostic Analytics. Thanks to Predictive Analytics, as it is now easy to identify tendencies, clusters and exceptions, while predicting future trends – all of this makes this analytics an extremely valuable tool of help. By employing numerous machine learning algorithms and statistical approaches, Insight Analytics eventually predicts the likelihood of an event happening in the future, but remember, these assumptions are based on predictions and probabilities, hence not 100% accurate. Big conglomerates like Amazon and Walmart leverage this high-in-value type of analytics to decipher future sales trend, customer behaviors, purchase patterns and lot more. Prescriptive Analytics: What is to be done? This is where Big Data and Artificial Intelligence gets into action. The main objective of Prescriptive Analytics is to prescribe what action is to be taken to address the future problem. It is the next stop after Predictive Analytics to help business understand the underlying reasons of complications and devise the best of course of action. It shares insights on possible results and outcomes that eventually maximize chief business metrics. It works by combining mathematical models, data and numerous business rules. The data can be external as well as internal, while business rules are boundaries, preferences, best practices and other restraints. Machine learning, natural language processing, operations research and statistics area few examples of mathematical models. Though complex in nature, Prescriptive Analytics when used by companies can have a huge impact on the overall operations and future business growth. The best example of this type of analytics is a traffic application that enables you to select the easiest route to home, after paying attention to the distance of the route, the speed of travelling and prevailing traffic constraints in the city you are travelling. The current trends highlight that an increasing number of companies are appreciating Big Data solutions and looking forward to Data Analytics implementation.However, it is just that they should select the right type of analytics solutions to enhance ROI, increase service quality and lessen operational costs. Do you have any other information or thought on this topic? Feel free to share with us by commenting below.
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4 Types Of Data Analytics To Improve Decision-Making

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  • by Eshika Roy
  • 27th Jul, 2017
  • Last updated on 07th Oct, 2019
  • 8 mins read
4 Types Of Data Analytics To Improve Decision-Making

If you are on CSE stack portal, there’s a good chance that you are already well acquainted with the general terms like ‘Data Analytics’, ‘Big Data’ and ‘Business Intelligence’ lead to different things in different circumstances. But have you thought what would be the right BI platform to hack through a wide number of solutions for business success?

In this article, I will knuckle down disambiguating the term ‘Data Analytics’ by splitting it down into 4 different types and aligning them with decision-making objectives.

Descriptive Analytics: What happened?

The commonest of the common type of Analytics, Descriptive Analytics offers the analyst a comprehensive view of key metrics and measures within an organization. It analyses the data available in real-time as well as historical data to derive meaningful insights regarding the future of a company. The main aim of this basic type of analytics is to discover the reasons behind pretentious success or failure in the past, as a result it is also known as ‘Reporting Bedrock’.

A business learns from its past behaviors, and draws inceptions based on those observations about its future outcomes, how they are going to affect. Descriptive Analytics is clouted the best when a business is on its way to understand the overall performance of the organization at an aggregate level and perceive the various aspects.

The best example of this would be a profit and loss statement. In the same way, analysts can possess data on a huge population of customers – delving deeper into mastering the demographic information of these customers can be classified as ‘descriptive analytics’.

Diagnostic Analytics: What made it happen?

The next stop to understand the intricacies of Data Analytics after Descriptive Analytics is Diagnostic Analytics. After assessing descriptive data, brilliant diagnostic analytical tools enable an analyst to go deeper into the problem, with the help of drilldowns and queries to eradicate the root-cause of the trouble. In simple words, in this analytics, historical data are ascertained against other data to reveal the answer of the question ‘why it happened’.

With Diagnostic Analytics, the companies are now able to make breakthroughs, to pick out the dependencies and to discern patterns. Organizations prefer this type of analytics as it gives them a deeper perception regarding a specific problem. On the other hand, the organizations should keep all the detailed information by their side, otherwise data collection may turn out to be time-consuming.

Effectively designed, well-integrated Business Information (BI) dashboards that assimilate the readings of time-series data, and participating filters and drilldown capabilities are deemed perfect for such analysis.

Predictive Analytics: What is going to happen?

It is all in the right predictions. Predictive Analytics involve analysis of past data patterns and trends to accurately forecast the future business outcome. It helps in determining realistic goals for the company and its effective execution and moderating expectations, by manipulating the findings of Descriptive and Diagnostic Analytics.

Thanks to Predictive Analytics, as it is now easy to identify tendencies, clusters and exceptions, while predicting future trends – all of this makes this analytics an extremely valuable tool of help. By employing numerous machine learning algorithms and statistical approaches, Insight Analytics eventually predicts the likelihood of an event happening in the future, but remember, these assumptions are based on predictions and probabilities, hence not 100% accurate.

Big conglomerates like Amazon and Walmart leverage this high-in-value type of analytics to decipher future sales trend, customer behaviors, purchase patterns and lot more.

Prescriptive Analytics: What is to be done?

This is where Big Data and Artificial Intelligence gets into action. The main objective of Prescriptive Analytics is to prescribe what action is to be taken to address the future problem. It is the next stop after Predictive Analytics to help business understand the underlying reasons of complications and devise the best of course of action.

It shares insights on possible results and outcomes that eventually maximize chief business metrics. It works by combining mathematical models, data and numerous business rules. The data can be external as well as internal, while business rules are boundaries, preferences, best practices and other restraints. Machine learning, natural language processing, operations research and statistics area few examples of mathematical models.

Though complex in nature, Prescriptive Analytics when used by companies can have a huge impact on the overall operations and future business growth. The best example of this type of analytics is a traffic application that enables you to select the easiest route to home, after paying attention to the distance of the route, the speed of travelling and prevailing traffic constraints in the city you are travelling.

The current trends highlight that an increasing number of companies are appreciating Big Data solutions and looking forward to Data Analytics implementation.However, it is just that they should select the right type of analytics solutions to enhance ROI, increase service quality and lessen operational costs. Do you have any other information or thought on this topic? Feel free to share with us by commenting below.

Eshika

Eshika Roy

Blog Author

Eshika Roy is a seasoned copywriter working for DexLab Analyticsby the day, and a hobbyist playing with numbers by the night. She brings to us this new future face of technology and how it would change our world. Beyond this she has an inclination for fiction novels, exploring different cuisines, and confectionery and dessert cooking.

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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. 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A new breed of ‘Fast Data’ architectures has evolved to be stream-oriented, where data is processed as it arrives, providing businesses with a competitive advantage. - Dean Wampler (Renowned author of many big data technology-related books)Dean Wampler makes an important point in one of his webinars. The demand for stream processing is increasing every day in today’s era. 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With Kafka Streams, spend predictions are more accurate than ever.Zalando: As the leading online fashion retailer in Europe, Zalando uses Kafka as an ESB (Enterprise Service Bus), which helps us in transitioning from a monolithic to a micro services architecture. Using Kafka for processing event streams enables our technical team to do near-real time business intelligence.Trivago: Trivago is a global hotel search platform. We are focused on reshaping the way travellers search for and compare hotels while enabling hotel advertisers to grow their businesses by providing access to a broad audience of travellers via our websites and apps. As of 2017, we offer access to approximately 1.8 million hotels and other accommodations in over 190 countries. We use Kafka, Kafka Connect, and Kafka Streams to enable our developers to access data freely in the company. Kafka Streams powers parts of our analytics pipeline and delivers endless options to explore and operate on the data sources we have at hand.Broadly, Kafka is suitable for microservices integration use cases and have wider flexibility.Spark Streaming Use-cases:Following are a couple of the many industries use-cases where spark streaming is being used: Booking.com: We are using Spark Streaming for building online Machine Learning (ML) features that are used in Booking.com for real-time prediction of behaviour and preferences of our users, demand for hotels and improve processes in customer support. Yelp: Yelp’s ad platform handles millions of ad requests every day. To generate ad metrics and analytics in real-time, they built the ad event tracking and analyzing pipeline on top of Spark Streaming. It allows Yelp to manage a large number of active ad campaigns and greatly reduce over-delivery. It also enables them to share ad metrics with advertisers in a timelier fashion.Spark Streaming’s ever-growing user base consists of household names like Uber, Netflix, and Pinterest.Broadly, spark streaming is suitable for requirements with batch processing for massive datasets, for bulk processing and have use-cases more than just data streaming. Dean Wampler explains factors to evaluation for tool basis Use-cases beautifully, as mentioned below: Sr.NoEvaluation CharacteristicResponse Time windowTypical Use Case Requirement1.Latency tolerancePico to Microseconds (Real Real time)Flight control system for space programs etc.Latency tolerance< 100 MicrosecondsRegular stock trading market transactions, Medical diagnostic equipment outputLatency tolerance< 10 millisecondsCredit cards verification window when consumer buy stuff onlineLatency tolerance< 100 millisecondshuman attention required Dashboards, Machine learning modelsLatency tolerance< 1 second to minutesMachine learning model trainingLatency tolerance1 minute and abovePeriodic short jobs(typical ETL applications)2.Evaluation CharacteristicTransaction/events frequencyTypical Use Case RequirementVelocity1M per secondNest Thermostat, Big spikes during specific time period.3Evaluation CharacteristicTypes of data processingNAData Processing Requirement1. SQLNA2. ETL3. Dataflow4. Training and/or Serving Machine learning modelsData Processing Requirement1. Bulk data processingNA2. Individual Events/Transaction processing4.Evaluation CharacteristicUse of toolNAFlexibility of implementation1. Kafka : flexible as provides library.NA2. Spark: Not flexible as it’s part of a distributed frameworkConclusionKafka Streams is still best used in a ‘Kafka -> Kafka’ context, while Spark Streaming could be used for a ‘Kafka -> Database’ or ‘Kafka -> Data science model’ type of context.Although, when these 2 technologies are connected, they bring complete data collection and processing capabilities together and are widely used in commercialized use cases and occupy significant market share. 
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Apache Spark Vs Apache Storm - Head To Head Comparison

In today’s world, the need for real-time data streaming is growing exponentially due to the increase in real-time data. With streaming technologies leading the world of Big Data, it might be tough for the users to choose the appropriate real-time streaming platform. Two of the most popular real-time technologies that might consider for opting are Apache Spark and Apache Storm. One major key difference between the frameworks Spark and Storm is that Spark performs Data-Parallel computations, whereas Storm occupies Task-Parallel computations. Read along to know more differences between Apache Spark and Apache Storm, and understand which one is better to adopt on the basis of different features. Comparison Table: Apache Spark Vs. Apache StormSr. NoParameterApache SparkApache Storm1.Processing  ModelBatch ProcessingMicro-batch processing2.Programming LanguageSupports lesser languages like Java, Scala.Support smultiple languages, such as Scala, Java, Clojure.3.Stream SourcesHDFSSpout4.MessagingAkka, NettyZeroMQ, Netty5.Resource ManagementYarn and Meson are responsible.Yarn and Mesos are responsible.6.Low LatencyHigher latency as compared to SparkBetter latency with lesser constraints7.Stream PrimitivesDStreamTuple, Partition8.Development CostSame code can be used for batch and stream processing.Same code cannot be used for batch and stream processing.9.State ManagementSupports State ManagementSupports State Management as well10.Message Delivery GuaranteesSupports one message processing mode: ‘at least once’.Supports three message processing mode: ‘at least once’, ‘at most once’, ‘exactly once’.11.Fault ToleranceIf a process fails, Spark restarts workers via resource managers. (YARN, Mesos)If a process fails, the supervisor process starts automatically.12.Throughput100k records per node per second10k records per node per second13.PersistenceMapStatePer RDD14.ProvisioningBasic monitoring using GangliaApache AmbariApache Spark: Apache Spark is a general-purpose, lighting fast, cluster-computing technology framework, used for fast computation on large-scale data processing. It can manage both batch and real-time analytics and data processing workloads.  Spark was developed at UC Berkeley in the year 2009. Apache Storm:Apache Storm is an open-source, scalable fault-tolerant, and real-time stream processing computation system. It is a framework for real-time distributed data processing, which focuses on stream processing or event processing. It can be used with any programming language and can be integrated using any queuing or database technology.  Apache Storm was developed by a team led by Nathan Marz at BackType Labs. Apache Spark Vs. Apache Storm1. Processing Model: Apache Storm supports micro-batch processing, while Apache Spark supports batch processing. 2. Programming Language:Storm applications can be created using multiple languages like Java, Scala and Clojure, while Spark applications can be created using Java and Scala.3. Stream Sources:For Storm, the source of stream processing is Spout, while that for Spark is HDFS. 4. Messaging:Storm uses ZeroMQ and Netty as its messaging layer while Spark is using a combination of Nettu and Akka for distributing the messages throughout the executors. 5. Resource Management:Yarn and Meson are responsible for resource management in Spark, while Yarn and Mesos are responsible for resource management in Storm. 6. Low Latency: Spark provides higher latency as compared to Apache Storm, whereas Storm can provide better latency with fewer restrictions.7. Stream Primitives:Spark provides with stream transforming operators which transform DStream into another, while Storm provides with various primitives which perform tuple level of processing at the stream level (functions, filters). 8. Development Cost:It is possible for Spark to use the same code base for both stream processing and batch processing. Whereas for Storm, the same code base cannot be used for both stream processing and batch processing.  9. State Management: The changing and maintaining state in Apache Spark can be updated via UpdateStateByKey, but no pluggable strategy can be applied in the external system for the implementation of state. Whereas Storm does not provide any framework for the storage of any intervening bolt output as a state. Hence, each application has to create a state for itself whenever required. 10. Message Delivery Guarantees (Handling the message level failures):Apache Spark supports only one message processing mode, viz, ‘at least once’, whereas Storm supports three message processing modes, viz, ‘at least once’ (Tuples are processed at least one time, but can be processed more than once), ‘at most once’  and ‘exactly once’ (T^uples are processed at least once). Storm’s reliability mechanisms are scalable, distributed and fault-tolerant. 11. Fault-Tolerant:Apache Spark and Apache Storm, both are fault tolerant to nearly the same extent. If a process fails in Apache Storm, then the supervisor process will restart it automatically, as the state management is managed by Zookeeper, while Spark restarts its workers with the help of resource managers, who may be Mesos, YARN or its separate manager.12. Ease of Development: In the case of Storm, there are effective and easy to use APIs which show that the nature of topology is DAG. The Storm tuples are written dynamically. In the case of Spark, it consists of Java and Scala APIs with practical programming, making topology code a bit difficult to understand. But since the API documentation and samples are easily available for the developers, it is now easier. Summing Up: Apache Spark Vs Apache StormApache Storm and Apache Spark both offer great solutions to solve the transformation problems and streaming ingestions. Moreover, both can be a part of a Hadoop cluster to process data. While Storm acts as a solution for real-time stream processing, developers might find it to be quite complex to develop applications due to its limited resources. The industry is always on a lookout for a generalized solution, which has the ability to solve all types of problems, such as Batch processing, interactive processing, iterative processing and stream processing. Keeping all these points in mind, this is where Apache Spark steals the limelight as it is mostly considered as a general-purpose computation engine, making it a highly demanding tool by IT professionals. It can handle various types of problems and provides a flexible environment to in. Moreover, developers find it to be easy and are able to integrate it well with Hadoop. 
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