Several Ways Big Data can Save or Destroy your Business

Nothing about Big Data is Small at all.

The internet pundits some 2 decades ago claimed the world to be becoming paperless and for all data present in the world going digital. With information piling up and computational capacities becoming increasingly heavy, the flow of data has significantly multiplied. As an outcome, for the management of this data, emerged a term called “Big data”. Big data is triggering an enormous change in the way businesses used to work. Companies are becoming heavily dependent on different tools for the management of big data.

Big data is the arrangement and organization of large volume of data. This data can be found in either structured or unstructured format. With massive information present in the market, the analysis and regulation of data get out of hands for many companies. However, big data management serves as a savior for such corporations that tend to accumulate a lot of information from different sources, for the purpose of market intelligence during the course of business. This data can be found in terabytes or petabytes.

The management of big data is the real determiner of a business’ success or failure. Proper gathering and management of information has a make or break effect on a company’s business model. It enables enterprises to search and analyze the data to find solutions to user-centric issues.

There are huge chunks of data and streams available in the market, therefore, the chances of missing on a great amount of important and critical information become high. As the need of market intelligence is increasing, the need for applications that focus on management of data services is gaining momentum too. There are many ventures taking the plunge, yet the question is if a company is actually willing to try luck in making its game better. Working with data analytics can be challenging for many companies as they require immense resources. The risks of loss are as high as the chances of gains.

Any entrepreneur can easily consider it a sign of intelligent business startup that it looks at the consistent players in the arena for muse. It is very vital for any business to look around and observe what trends the other actors in the market are following. This gives businessmen ideas to run the venture successfully and generate profit. It is extremely important that businessmen keep eyes fixed on the activity of the market and see how other fellows are making positive use of the data.

Study and analyze the ins and outs of venture area.

The major reason behind companies failing because of big data is their inability to remain focused on the correct data. Too much data and on top of that, irrelevant data is the recipe to fail a venture you are trying your luck at. In order to succeed, a company is required to properly investigate and study the patterns contemporary market actors are making use of their data through. A company should not just study the models of successful businesses but also those that failed miserably. This is the way they can get proper insight and understand the pitfalls they need to avoid in their endeavors.

It has been observed that marketers mistake in managing and identifying the right kind of data and use irrelevant heavy data in the business. This exhausts the customer when trying to find the required information from the pool of random unnecessary data consuming space. Corporations need to educate employees regarding the management of data to avoid any failure.

Migration.

Heads of different corporations collectively called migration the reason for their usage of low quality data in business. It was also found that more than most of the data migration costs above the estimated amount and take longer time. So, it should not be believed that migration is an easy process, it requires only the technically skilled to complete the task.

Companies should take help of specialists from all departments inclusive of the real data users. It should be made sure that they are familiar with the processes of migration and data management. They know about people with access to the data and how those people are making use of it. The specialists should ensure that data migration takes place step by step, which means that all requirements for the migration are met during and after the process. It has been observed that companies that try to migrate in haste usually cause themselves bigger troubles and financial losses. It takes them more time fixing these issues than allocating proper attention to the step by step process of migration.

A company before migrating data should consider some elements like redefining data and checking the quality of it. It should make a map of all strategies and techniques, also take into account the scope and budget of the data movement.

How to Avoid Internal Collision.

Giant corporations have large data distributed in different places of the organization. Each form of data serves a unique purpose and is regulated by different sets of people. There is hardly any communication among the users of that data as each form of data is relevant to its concerned department.

This can be extremely helpful for many companies as there is no internal collision of data used by different departments. Devolution data is a great step in the management of big data. This step minimizes the chances of a bottleneck situation within the company’s various departments. Segregating big data into sections is the most important function of the management of data. It is a delicate business and requires extremely balanced approach to separate data into right categories.

If data is not stored in the right way, it can affect the decision making of a company, therefore, its considered an important move in the data management and eventually for company’s business results. It is recommended that data is stored in the form of dashboard, reports, services etc. it should be filtered as per the data roles into categories varying from least to most important or however the organization deems fit. However, the main idea behind this division is splitting big data into exclusive smaller sections so, the data consumers can make best use of the available data.

Incorrect Data.

Employees that work with data know that mismanagement of data can leave irreparable effects on the decision making of a company. This can also shake customers’ trust in the company.

Invasive Data Mining

Invasive data use against a customer can infuriate him. A company should follow these ethical rules as principle policies to not use any customer’s information or make predictions regarding their situations based on their purchases. The basic information of a customer’s purchase should be kept secret from people, as violating this ethical code is tantamount to invasion of your customer’s privacy.

Some great online startup business ventures that transformed the way data was perceived.

Data analytics is a big growing industry with many potential investors and companies aiming at developing their business through it. There are many new but already renowned businesses that are leading the way for instance Uber, Foursquare, Spotify and Feedzai.

These companies have been using big data to their advantage and making best use of it.

Final Word :

Big data, market intelligence is one of the fastest growing techniques in the world. This helps the trader assess the market and understand the needs of customers.

Basic information of bigdata

What is Big Data?

Data provides information. Accumulation of information is equivalent to accumulation of power and achieving more control over the related events and results. Enormous volume of data with diverse nature is generated In the modern world  that storing them and analysing them to get the required output had become a big challenge. The data could be anything from a real time transaction, climatic conditions, clicks on computers, mobile logs, posts or tweets from social media and much more. If the data so collected becomes impossible for a single machine store and process then such data could be named as Big Data.

Data which are very large in size is called Big Data. Normally we work on data of size MB(WordDoc ,Excel) or maximum GB(Movies, Codes) but data in Peta bytes i.e. 10^15 byte size is called Big Data. It is stated that almost 90% of today's data has been generated in the past 3 years.

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Steps to Big Data Success

By Sudhi Sinha, Vice President of Product Development, Building Technology & Services, Johnson Controls

A great convergence occurred several years ago in information technology, with the cost and capacity of computer storage, processing, and networking all improving at the same time. This trifecta brought us to an inflection point where new technologies such as Hadoop could unlock unprecedented volumes of data and deliver new business value. That is the essence of the much-talked-about phenomenon known as “big data.”

In practical terms, big data means it is now feasible to collect, store, and use all available data, leading to new insights derived from ever-more sophisticated analytics. This presents a great opportunity to increase the value of existing data while also preparing the ground for the deluge of additional data expected to be unleashed by the Internet of Things, or IoT— the network of information generated by people, objects, and the environment.

How did we get to this place? Historically, large data sets needed to be structured and stored in a very specific way in order to then apply queries and analytics. From the columns and rows of what conceptually was much like a vast spreadsheet, deriving insights was an inflexible process where the very structure of the data was predefined and tended to make the range of conclusions all but inevitable.  In that environment, out-of-the-box thinking was almost impossible.

Today, big data technologies have overcome the limitations of past practices by breaking data sets into multiple components, distributing and analyzing those components on different processors, and then aggregating the results to deliver a meaningful picture of the whole that is not constrained by a specific, preordained order or structure.

As the ability to use all that data and run analytics on it has increased and improved, businesses are suddenly able to access new types of insights. It is the “art of the possible” for data.  Furthermore, now that the analysis encompasses all the data rather than just samples, the results are more accurate and   you can look at data in connection with more scenarios, sources, geographies, and applications. Your view of what is possible starts to broaden.

Combining Big Data and the IoT

Big data technologies are maturing, and they are widely applied across many industries. In automotives, for example, a manufacturer can incorporate data from warranty and maintenance activities or even a customer’s visit to the auto body shop to better understand patterns of failure.

Collecting data from dealers in different states is not new. But big data takes this activity to the next level. For example, marrying the actual weather data in a region to vehicle failure information allows manufacturers to begin developing predictive analytics. Somewhat simplistically, cars in Massachusetts may be found to suffer more cold-induced battery failures while vehicles in Texas may see more problems with air-conditioners.  But folding in other data streams flowing from the Internet of Things enables more nuanced predictions. Think how the somewhat obvious prediction that cars in cold climates suffer from battery failures and in hot climates experience air-conditioner problems can be enhanced by factoring in local traffic information, say, or sensor data from the car itself.

Consumer data is central to retail operations and to the airline, ticket, and travel industries. In these instances, you are trying to understand demographics and individual consumers in the context of location, season, trends, and so on. Big data is a powerful tool for bringing together what you know about those factors and integrating new sources of information – for example, sensors in a retail facility or weather for a travel site – to produce deeper insights.

Putting Big Data to Work in Your Business

To realize the value of big data and capitalize on the emerging  Internet of Things, follow this step-by-step approach:

1. Build a Strategy Framework.
  It is vital to develop a clear conceptual understanding of big data and how its potential might map to your own organization’s needs.  This should probably begin with a review of existing business intelligence efforts and data sources. But don’t stop there; look further and see whether there might be other data sources that aren’t being utilized or processes that could be better instrumented to provide valuable data. Remember, big data means BIG. More is generally better.  Think about the kind of insights big data might be able to provide, and then get ready for the next steps, when you will move ahead and engage the organization.

2. Create an “Opportunity Landscape.” 
 If the full potential of big data is a gold mine, the tactical projects that can get you started are gold coins.  The point is to focus initially on a small number of projects or initiatives with the potential for significant payback.  These could be aspects of your business that have not performed as expected and where data is available to potentially change the game.  When you’ve found the gold in those projects, you will be better able to gather resources to finance the gold mine and begin to extract widespread rewards for the organization.

3. Effectively Manage Big Data Projects. 
This means having a good grasp on the learning that may be transferable from other transformation and IT projects, as well as focusing on what is really unique.  Depending on the existing capabilities of your organization, big data may require investments in human capital—the IT experts and data scientists who can help ensure that you get the desired results. Because big data is by definition an evolving activity, having a robust project-management framework can help you keep things on track and moving in the right direction. That approach can also help you steer the initiative toward new and emerging opportunities.

4. Build the Right Technology Landscape. Even if you are an IT professional, the nuances associated with big data may be eye opening.  A big data initiative does not have to “break the bank” but it will likely require some specific investments.  The good news is that big data initiatives are generally less expensive and less complex than the massive data-warehouse projects that some organizations have built using traditional IT tools—a sort of “brute force” attempt to garner more value from corporate data. The best news is that starting small means your initial expenses can be quite low.

5. Build a Winning Team.
 Even in an era where technology is so critical, the human factor can make the difference between success and failure. Big data projects call upon a range of skills. Obviously, some of these are pure IT skills; others have to do with mastering the data science side of big data. But deep business knowledge is also vital – having the ability to dig into operational realities and discern issues that can be attacked with big data. Recruiting the people you will need for your big data projects, and then organizing, managing, and motivating them, is a case of making investments up front that will pay over the long term and yield future success.

6. Manage Your Investments and the Monetization of Your Data.  
The valuation and monetization of data is almost a “secret sauce” aspect of big data.  Having an approach to help you weigh the costs of big data with its benefits can help you make decisions that are most likely to be profitable and meaningful. This inquiry starts at a granular level and helps you develop an appreciation of the true value of data and its costs.  If you take this path you’ll never look at gigabytes the same way again, and you’ll begin to develop a practitioner’s instincts for harnessing information cost-effectively.

7. Effectively Drive Change.
 Your initial big data steps may not rock the boat too much, but over time, big data can upend assumptions, sometimes even assumptions that have been driving the entire business.  With big data, you have the potential to cut through the fog and know the things that have always seemed unknowable.  If that sounds hard to believe, think about how much data is not used effectively now. Add to that the new data that may become available in the near future, through sensors, better use of existing systems, and new external sources. To ensure that all of this data helps the organization, plan to implement change-management practices and start teaching your organization how to be more agile, adapting quickly to new assumptions driven by fresh insights.

8. Communicate Effectively.  
Change can only be mastered with effective communication. This isn’t simply a one-way street.  Listening is vital, too.  For an organization to ride the big data wave successfully, everyone should be “on board.” Only a small percentage of the organization may need new skills, but because big data can alter how business is conducted, everyone needs to be aware that change is afoot and to understand their stake in the future.

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In the final analysis, big data is about opportunity. It is the embodiment of the old adage, “knowledge is power.” By rebuilding your business upon the collection and analysis of big data – data that will expand exponentially as the Internet of Things emerges and matures – you can create a better future.

Different ways that big data is transforming marketing and sales

With the Internet going mainstream 20 years ago, big data is transforming marketing and sales like never before.

Today, marketers and sales leaders are in the midst of a major technology-driven transformation. We have access to a flood of data, giving us visibility into customer behavior and effectiveness of our marketing programs. However, we are a creating a new silo of data with every marketing application that goes live. And so what we lack is a visibility into the end-to-end customer journey or the aggregated customer view. We need to know better, and this is why it is crucial to understand how big data is transforming marketing and sales.

Ways how big data is transforming marketing and sales

Identifying valuable opportunities

In order to discover opportunities, you need to pull in relevant data sets; not just from within the company, but also outside it. Once you have the data, next comes analytics. And analytics leaders believe in ‘destination thinking’, and not mass analysis of all the data collected. Destination thinking involves writing, in simple sentences, the questions you need answers for or the business problems you wish to solve. Big data and its analysis is transforming marketing and sales by going beyond the broad and vague goals, into a level of specificity. For example, a company may have 20 percent of the overall market, a micro market analysis may reveal that while it has 60 percent of share in some markets, its share in others may be as little as 10 percent.

Starting with the consumer decision journey

Consumers today surf multiple channels and use an array of devices, technologies, and tools to fulfill a task. Data collected from these sources is critical to understanding the decision journey of a customer; this helps in not just identifying new customers, but also retaining the existing ones. Let’s take the example of B2B companies understand the importance of mapping customer decision journey in marketing and sales outcomes. 35 percent of B2B pre-purchase activities are digital in nature, and so B2B companies need to invest in websites that can effectively communicate the value of their products, SEO technologies to find potential customers and social media for spotting new sales opportunities. The underlying idea is for marketing and sales leaders to use big data for forming complete pictures of their customers; thereby, creating products and messages that are relevant to them. Big data helps you gain clarity, deliver more personalized products and services, up the ROI on marketing spend, and lift sales.

Adding speed and simplicity

The rate at which data is growing worldwide is proving to be quite daunting for most marketing and sales leaders. However, approaches like predictive statistics, natural language mining, and machine learning that allow for processing of vast amounts of data, employing a self-learning process, help create better and more relevant interactions with customers. For this, companies need to invest in what is called as algorithmic marketing. Algorithmic marketing uses big data and lends speed and simplicity to your marketing and sales activities. For example, you cannot just track keywords automatically, but also update them every 15 seconds based on ad costs, customer behavior, or change in search terms used. Advanced analytics, when applied to big data, not just speeds up your efforts at marketing and sales, but also shields your customer service operator or field sales representative, from analytical complexity; all you have in hand are simple guidelines and recommended actions. Big data is a goldmine for marketing and sales leaders, waiting to be mined for umpteen possibilities. You need to think beyond immediate revenues to make the most of it.

Top Most and Hot Big Data Technologies

Forrester’s TechRadar methodology evaluates the potential success of each technology and all 10 above are projected to have “significant success.” In addition, each technology is placed in a specific maturity phase—from creation to decline—based on the level of development of its technology ecosystem. The first 8 technologies above are considered to be in the Growth stage and the last 2 in the Survival stage.

Here is the 10 hottest big data technologies based on Forrester’s analysis:

1. Predictive analytics: software and/or hardware solutions that allow firms to discover, evaluate, optimize, and deploy predictive models by analyzing big data sources to improve business performance or mitigate risk.
2. NoSQL databases: key-value, document, and graph databases.
3. Search and knowledge discovery: tools and technologies to support self-service extraction of information and new insights from large repositories of unstructured and structured data that resides in multiple sources such as file systems, databases, streams, APIs, and other platforms and applications.
4. Stream analytics: software that can filter, aggregate, enrich, and analyze a high throughput of data from multiple disparate live data sources and in any data format.
5. In-memory data fabric: provides low-latency access and processing of large quantities of data by distributing data across the dynamic random access memory (DRAM), Flash, or SSD of a distributed computer system.
6. Distributed file stores: a computer network where data is stored on more than one node, often in a replicated fashion, for redundancy and performance.
7. Data virtualization: a technology that delivers information from various data sources, including big data sources such as Hadoop and distributed data stores in real-time and near-real time.
8. Data integration: tools for data orchestration across solutions such as Amazon Elastic MapReduce (EMR), Apache Hive, Apache Pig, Apache Spark, MapReduce, Couchbase, Hadoop, and MongoDB.
9. Data preparation: software that eases the burden of sourcing, shaping, cleansing, and sharing diverse and messy data sets to accelerate data’s usefulness for analytics.
10. Data quality: products that conduct data cleansing and enrichment on large, high-velocity data sets, using parallel operations on distributed data stores and databases.

Best Big Data Companies

Tableau

Originally spun out of Stanford University as a research project, Tableau started out by offering visualization techniques for exploring and analyzing relational databases and data cubes and has expanded to include Big Data research. It offers visualization of data from any source, from Hadoop to Excel files, unlike some visualization products that only work with certain sources, and works on everything from a PC to an iPhone.

New Relic

New Relic uses a SaaS model for monitoring Web and mobile applications in real-time that run in the cloud, on-premises, or in a hybrid mix. It uses more than 50 plug-ins from technology partners to connect to its monitoring dashboard. The plug-ins include PaaS/cloud services, caching, database, Web servers and queuing. Its Insights software for analysis works across the entire New Relic product line, and the company offers a product called Insights Data Explorer that is designed to make it easier for everyone on a software team to explore Insights events.

Alation

Alation crawls an enterprise to catalog every bit of information it finds and then centralizes the organization's knowledge of data, automatically capturing information on what the data describes, where the data comes from, who's using it and how it's used. In other words, it turns all your data into metadata, and allows for fast searches using English words and not computer strings. The company's products provide collaborative analytics for faster insight, a unified means of search, provides a more optimized data structure of the company's data, and assists in better data governance.

Teradata

Teradata has built a portfolio of Big Data apps into what it calls its Unified Data Architecture, which includes Teradata QueryGrid, Teradata Listener, Teradata Unity and Teradata Viewpoint. QueryGrid provides a seamless data fabric across new and existing analytic engines, including Hadoop. Listener is the primary ingestion framework for organizations with multiple data streams, Unity is a portfolio of four integrated products for managing data flow throughout the process, and Viewpoint is a custom Web-based dashboard of tools to manage the Teradata environment.

VMware

VMware has incorporated Big Data into its flagship virtualization product, called VMware vSphere Big Data Extensions. BDE is a virtual appliance that enables administrators to deploy and manage the Hadoop clusters under vSphere. It supports a number of Hadoop distributions, including Apache, Cloudera, Hortonworks, MapR and Pivotal.

Splunk

Splunk Enterprise started out as a log analysis tool but has since expanded its focus and now focuses on machine data analytics to make the information useable by anyone. It can monitor online end-to-end transactions, study customer behavior and usage of services in real time, monitor for security threats, and identify spot trends and sentiment analysis on social platforms.

IBM

Besides its mainframe and Power systems, IBM offers cloud services for massive compute scale through its Softlayer subsidiary. On the software side, its DB2, Informix and InfoSphere database software all support Big Data analytics and Cognos and SPSS analytics software specialize in BI and data insight. IBM also offers InfoSphere, the basic platform for building data integration and data warehousing used in a BD scenario.

Striim

Formerly known as WebAction, Striim is a real-time, data streaming analytics software platform that reads in data from multiple sources such as databases, log files, applications and IoT sensors and allows customers to react instantly. Enterprises can filter, transform, aggregate and enrich data as it is coming in, organizing it in-memory before it ever lands on disk.

SAP

SAP's main Big Data tool is its HANA in-memory relational database, which the company says can run analytics on 80 terabytes of data and integrates with Hadoop. Although HANA is a row-and-column database, it can perform advanced analytics, like predictive analytics, spatial data processing, text analytics, text search, streaming analytics, and graph data processing and has ETL (Extract, Transform, and Load) capabilities.

While some companies specialize in one or few sources of data, SAP deals with data from a wide range of sources, including data from sensors, machine logs and other equipment; human generated data – social, point of sale (POS), ERP, emails documents and other things that make up enterprise data.

Alpine Data Labs

A creation of Greenplum employees, Alpine Data Labs puts an easy-to-use advanced analytics interface on Apache Hadoop to provide a collaborative, visual environment for building analytics workflow and predictive models that anyone can use, rather than requiring a high-priced data scientist to program the analytics.

Oracle

Oracle has its Big Data Appliance that combines an Intel server with a number of Oracle software products. They include Oracle NoSQL Database, Apache Hadoop, Oracle Data Integrator with Application Adapter for Hadoop, Oracle Loader for Hadoop, Oracle R Enterprise tool, which uses the R programming language and software environment for statistical computing and publication-quality graphics, Oracle Linux and Oracle Java Hotspot Virtual Machine.

Alteryx

Calling itself the leader in self-service data analytics, Alteryx's software is meant for the business user and not the data scientist. It allows them to blend data from multiple and potentially disparate sources, analyze it and share it so that actions can be taken. Queries can be made from anything from a history of sales transactions to social media activity.

Splice Machine

Splice Machine bills itself as the provider of the only Hadoop relationship database management system (RDBMS). It can act as a general-purpose database that can replace Oracle, MySQL or SQL Server databases for various workloads on Hadoop. The latest version, 2.0, added Spark, which does all analytics in memory instead of on disk. Version 2.0 also added the ability to route work to one of two processing engines either OLTP or OLAP.

Pentaho

Pentaho is a suite of open source-based tools for business analytics that has expanded to cover Big Data. The suite offers data integration, OLAP services, reporting, a dashboard, data mining and ETL capabilities.

Pentaho for Big Data is a data integration tool based specifically designed for executing ETL jobs in and out of Big Data environments such as Apache Hadoop or Hadoop distributions on Amazon, Cloudera, EMC Greenplum, MapR, and Hortonworks. It also supports NoSQL data sources such as MongoDB and HBase. The company was acquired by Hitachi Data Systems in 2015 but continues to operate as a separate subsidiary.

SiSense

SiSense sells its Prism to the largest enterprises and some SMBs alike because of its small ElastiCube product, a high-performance analytical database tuned specifically for real-time analytics. ElastiCubes are super-fast data stores that are specifically designed for extensive querying. They are positioned as a cheaper alternative to HP's Vertica systems.

Thoughtworks

Thoughtworks incorporates Agile software development principals into building Big Data applications through its Agile Analytics product. Agile Analytics helps companies build applications for data warehousing and business intelligence using the fast paced Agile process for quick and continuous delivery of newer applications to extract insight from data.

Tibco Jaspersoft

Tibco's Jaspersoft subsidiary has introduced an hourly offering on Amazon's Cloud where you can buy analytics starting at $0.48 per hour. The company is also big on embedded its analytics – having done so with 130,000 production applications worldwide, used by organizations such as Red Hat, CA, Verizon, Tata, Groupon, British Telecom, Virgin, and the U.S. Navy.

Amazon Web Services

Amazon has a number of enterprise Big Data platforms, including the Hadoop-based Elastic MapReduce, Kinesis Firehose for streaming massive amounts of data into AWS, Kinesis Analytics to analyze the data, DynamoDB big data database, NoSQL and HBase, and the Redshift massively parallel data warehouse. All of these services work within its greater Amazon Web Services offerings.

Most significant, AWS is attempting to woo legacy database customers to its newer offering. Experts disagree on how successful AWS will be in this effort, but it is clearly a highly aggressive competitive move.

Microsoft

Microsoft's Big Data strategy is fairly broad and has grown fast. It has a partnership with Hortonworks and offers the HDInsights tool based for analyzing structured and unstructured data on Hortonworks Data Platform. Microsoft also offers the iTrend platform for dynamic reporting of campaigns, brands and individual products. SQL Server 2016 comes with a connector to Hadoop for Big Data processing, and Microsoft recently acquired Revolution Analytics, which made the only Big Data analytics platform written in R, a programming language for building Big Data apps without requiring the skills of a data scientist.

Google

Google continues to expand on its Big Data analytics offerings, starting with BigQuery, a cloud-based analytics platform for quickly analyzing very large datasets. BigQuery is serverless, so there is no infrastructure to manage and you don't need a database administrator, it uses a pay-as-you-go model.

Google also offers Dataflow, a real time data processing service, Dataproc, a Hadoop/Spark-based service, Pub/Sub to connect your services to Google messaging, and Genomics, which is focused on genomic sciences.

Mu Sigma

Mu Sigma offers an analytics services framework that looks at tables and tables and answers questions for the firm on issues like improved sales and marketing. It cleans up client data to show only relevant data, uses the data to understand it, generates insights from it and gives recommendations to the client. Mu Sigma tries to understand how the business actually works and then identifies where the problem actually is.

HP Enterprise

HP Enterprise has built up a considerable portfolio of Big Data products in a very short time. Its main product is the Vertica Analytics Platform, designed to manage large, fast-growing volumes of structured data and provide very fast query performance on Hadoop and SQL Analytics for petabyte scalability.

HPE IDOL software provides a single environment for structured, semi-structured and unstructured data. It supports hybrid analytics leveraging statistical techniques and Natural Language Processing (NLP).

HPE has a number of hardware products, including HPE Moonshot, the ultra-converged workload servers, the HPE Apollo 4000 purpose-built server for Big Data, analytics and object storage. HPE ConvergedSystem is designed for SAP HANA workloads and HPE 3PAR StoreServ 20000 stores analyzed data, addressing existing workload demands and future growth.

Big Panda

BigPanda offers a data science algorithm-based platform specifically for IT and DevOps staff that is specifically geared toward addressing alert overload. One of the many sources of Big Data is logs, and they can quickly get out of hand with redundant or false alerts. The company noticed that developers were being overwhelmed with alerts from their logs and had no idea which were real and which were false flags. BigPanda filters down that overload to just the meaningful alerts, allowing IT to react quicker to real problems.

Cogito

A highly vertical but important service, Cogito Dialog uses behavioral analytics technology, including analysis of everything from customer emails to social media to analysis of the human voice, to help phone support personnel improve their communications while on the phone with customers and to help organizations better manage agent performance.

Datameer

Datameer claims its end-to-end data analytics solution for Hadoop enables business users to discover insights in any data via wizard-based data integration, iterative point-and-click analytics, and drag-and-drop visualizations, regardless of the data type, size, or source.

Common Myths Around Virtualizing Big Data

Big data burst on to the scene a little over a decade ago. Today it is not an obscure term confined to just a handful of bleeding edge companies. It is a mainstream trend that every enterprise undergoing a digital transformation journey has adopted. The technology landscape around big data has broadened dramatically; in the early days it meant Apache Hadoop, today it includes Apache Spark and NoSQL databases like MongoDB and Apache Cassandra among many other new technologies.

Myth 1: Virtualizing big data applications is fine for development but not for production

It is true that software engineers have used virtual infrastructure to develop big data applications over the last several years. However, these big data applications have now also made their way into production. Virtualized applications make it possible for various users, including business analysts and data scientists, to work on different data analysis tasks simultaneously, resulting in significant productivity increases of these teams

Myth 2: There’s a performance penalty when virtualizing Hadoop

Misperceptions about the performance of virtualized Hadoop still remain, but it should be a moot point by now. Since 2011, performance benchmarks have consistently shown that running Hadoop on virtual machines is as performant, or more, as running Hadoop on physical machines, with results showing that Map Reduce jobs completed up to 12 percent faster and Spark/Machine Learning jobs up to 10 percent faster. The latest performance benchmarks, performed by VMware in 2016, show that Hadoop scales amply on virtual machines with similar overall performance to bare metal and distinct advantages when it comes to utilization of cluster resources.

Myth 3: You need a SAN for virtualizing Hadoop, but can Hadoop even use a SAN?

These myths are related, so let’s tackle them both. First of all, it is a misperception that the basic features of virtual machines require a SAN. It is common for enterprises to use non-shared direct-attached storage to host Hadoop data in the virtual machines attached to that storage. Vendors in the space both support and often recommend direct-attached storage for performance benefits and cost savings.

Secondly, if you want to take advantage of shared storage solutions like a physical SAN or virtual SAN such as VSAN, then Hadoop not only works, but many users prefer to use a SAN to begin their first Hadoop experiments, often because it is a core part of their infrastructure, and was in place when the enterprise first started to adopt Hadoop.

Myth 4: You can only run the traditional Hadoop stack but not the latest and greatest tech

In many ways Hadoop has become a catchall for big data, but it is a misleading one. At its outset just over a decade ago, Hadoop meant Hadoop Distributed File System and several other tools to consume data from it like MapReduce, Hive and Pig. Today, it encompasses many projects, with other big data projects often dragged into the net. However, Apache Spark is distinct from Hadoop (although it integrates with it), and offers faster and more efficient means to analyze ever-growing volumes of data. The performance benchmark paper cited earlier shows comparable performance between Apache Spark running on virtual machines or bare metal.

It is also not only possible, but common, to find enterprise users running different versions of Hadoop and Spark from multiple Big Data vendors in separate clusters running on virtual machines within the same grouping of hardware.

Myth 5: The hot tech is containers so you should use that instead of VMs

Container technology like Docker is white hot at the moment, and for good reason. It is becoming a popular choice with cutting edge developers because it is easy to use and lightweight. They have quickly become standard operating procedure in many development houses. However, it is important to understand the right use cases for using containers with a big data strategy. Containers are best suited to hold the Compute side of Hadoop – the part that executes your algorithms, such as the NodeManagers of YARN and Executors of Spark. Containers require you to separate out your data storage to a different place. Holding terabytes of data in a container is not the accepted wisdom today. So when applying virtualization to this, the containers are executing in a virtual machine, either one to one with the virtual machine or one to many, where the data is retrieved by the VM. If high levels of security are an enterprise focus then isolation of concerns and users is more optimal through virtual machines. The combination of virtual machines and containers brings mature operations management to the challenges of handling containers in production.