- What Is Big Data Analysis
- Ppt On Big Data
- What Is Big Data In Healthcare
- What Is Big Data Technology
- What Is Big Data Technology
- What Is Big Data Analytics
Oracle Big Data Service is a Hadoop-based data lake used to store and analyze large amounts of raw customer data. As a managed service based on Cloudera Enterprise, Big Data Service comes with a fully integrated stack that includes both open source and Oracle value. Commercial Lines Insurance Pricing Survey - CLIPS: An annual survey from the consulting firm Towers Perrin that reveals commercial insurance pricing trends. It tracks prices charged by over 30. Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity. Intelligent Decisions. Data privacy – The Big Data we now generate contains a lot of information about our personal lives, much of which we have a right to keep private. Increasingly, we are asked to strike a balance between the amount of personal data we divulge, and the convenience that Big Data-powered apps and services offer.
This article answers the question – what is big data?
Big Data is getting bigger. This market growth has led to a proliferation of technology vendors touting themselves as “Big Data solution providers.” In fact, the volume and variety of Big Data vendors seems to rival the volume and variety of Big Data itself, which can make the selection of technology partners a daunting task for any business pursuing a digital transformation strategy.
To help ensure you have a productive and mutually-beneficial conversation with Big Data technology vendors, it’s critical that you first fully understand:
- What is Big Data?
- The key components of the Big Data ecosystem, which include infrastructure, analytics, applications, data sources and application programming interfaces (APIs)
Here’s everything you need to know.
What is Big Data?
A helpful way to think about Big Data is to compare it to its forerunner: the Enterprise Data Warehouse (EDW). An EDW ingests multiple data sources into a common format using Extraction/Transformation/Load (ETL) tools and best practices. The inbound data tends to be well-structured and needs to be converted from its native format to the EDW format. The processing of data into and provisioning of data out of an EDW tend to happen in the same technical environment (usually the same server).
In contrast, Big Data processing can happen in many different environments and can ingest many different types of structured and unstructured data. When thinking about Big Data, consider the “seven V’s”:
- Volume
Big Data is, well … big! With the dramatic growth of the internet, mobile devices, social media, and Internet of Things (IoT) technology, the amount of data generated by all these sources has grown accordingly. - Velocity
In addition to getting bigger, the generation of data and organizations’ ability to process it is accelerating. - Variety
In earlier times, most data types could be neatly captured in rows on a structured table. In the Big Data world, data often comes in unstructured formats like social media posts, server log data, lat-long geo-coordinates, photos, audio, video and free text. - Variability
The meaning of words in unstructured data can change based on context. - Veracity
With many different data types and data sources, data quality issues invariably pop up in Big Data sets. Veracity deals with exploring a data set for data quality and systematically cleansing that data to be useful for analysis. - Visualization
Once data has been analyzed, it needs to be presented in a visualization for end users to understand and act upon. - Value
Data must be combined with rigorous processing and analysis to be useful.
Infrastructure
Broadly speaking, Big Data infrastructure includes the hardware and software “plumbing” that gathers and organizes data. These foundational pieces include data collection, data storage, data access and data processing.
What Is Big Data Analysis
Data Collection
Collecting data simply means bringing it into your enterprise for further organization, processing and analysis. Much of this data you may already have in-house, such as data from point-of-sale systems, customer databases, customer relationship management systems, and financials. You may also have a need to collect external data from social media threads or third-party data sources, which could require a bit of technical development to acquire.
Data Storage
Storage can be as simple as a computer hard drive or even a flash memory stick. But in the Big Data world, there is almost always a need to manage multiple points (or nodes) of data storage and processing. This is where a toolset like Hadoop comes into play. Hadoop components provide a framework for keeping track of stored data files on multiple nodes, breaking up data for large computations to multiple nodes, and assembling the results, as well as node management functions like scheduling automated jobs.
Data Access
Marked 2 5 39 resz. Before the advent of Big Data, Structured Query Language (SQL) was the common language of the data world. SQL enables users to access structured, relational databases to retrieve data with emphasis on consistency and reliable transactions. Of course, with Big Data, much of the data is unstructured as described above. This is where newer data access tools like NoSQL (short for “Not Only SQL”) come into play. While SQL databases are rigorously structured and reside on a single server, NoSQL databases house unstructured data across multiple servers and emphasize speed and scalability.
Data Processing
A tool like Massively Parallel Processing (MPP) breaks up the processing of large data sets across multiple nodes and concurrently manages the needed processing for a single application. You could think of MPP as the general contractor for a house. The general contractor is in charge of the entire project but farms out the foundation work, framing, electrical, plumbing, HVAC, roofing and interior work to subcontractors. In a similar manner, MPP manages the completion of a large data processing task, but farms out the processing of certain tasks to separate nodes, only to reassemble the results when all processing is complete.
Analytics
With the foundational infrastructure and ability to retrieve and process large amounts of data from many different sources in place, Big Data can now be analyzed for business insight. Analytics involve data preparation, modeling and visualization.
Ppt On Big Data
Data preparation involves combining disparate data sources into a common format. Successfully preparing data requires in-depth, exploratory analysis to understand the data types and data quality issues of the source data, as well as a well-thought-out target data model.
With properly-prepared data in hand, analysts and data-savvy business users can develop their analytics model. This is where the business problem meets the data. The model can be something as basic as summarizing sales data by region. But more sophisticated analyses, like machine-learning algorithms, are well-suited to Big Data’s ability to manage and process massive amounts of information.
Bringing analytic insights to life for non-technical users is the domain of visualization. In the hands of skilled business intelligence authors, visualization tools take on an artistic quality while presenting conclusions from the aforementioned analytic insights.
Applications
Industry-specific applications bring business insights to life in an actionable and relevant way for business users. These applications combine the technical architecture, data engineering, data science and analytics of Big Data with industry insight to provide compelling solutions to real-world business problems. The examples below are a taste of what Big Data can accomplish.
Faster Time to Market
Like all pharmaceutical companies, Bristol-Myers Squibb (BMS) is interested in reducing the length of clinical trials to bring new products to market more quickly. Using an Amazon Web Services (AWS) portal to host computational-heavy simulations, BMS removed the bottleneck of its slow and congested internal research system in favor of a cloud-based, scalable solution. Folx pro 5 7 mac. Prior to moving to a Big Data environment, BMS scientists took 60 hours to run a few hundred simulations. After the move to AWS, scientists can run up to 2,000 simulations in only 1.2 hours.
Increased Sales Through Personalized Marketing
Kroger accomplished a rare feat in retail marketing: they realized a 70% direct mail return rate when the average is 3.7%. Using a Big Data approach enabled Kroger to record the purchase history of each individual customer. Then, using predictive analytics, they were able to create mailers specific to that customer. According to Nishat Mehta, an executive at dunnhumby, Kroger’s customer data partner, “We make decisions not based on what you bought today but what you have bought over the last two years. We can recommend a product you buy every four months. You don’t have to know, but we know.”
Optimizing Customer Value
Avis Budget Group’s foray into Big Data has increased market share and improved customer loyalty. Using a remarkable combination of diverse data sources, Big Data technologies and data science, Avis Budget can make a fact-based assessment of a customer’s value and market segment. Avis Budget’s Big Data platform combines corporate affiliation data, demographics, rental history, service history, customer feedback forms and even social media data to feed a predictive model that estimates each customer’s lifetime value. Armed with these insights, Avis Budget marketers created graduated incentives to move each customer segment along the path of greater customer loyalty. This effort yielded an estimated $200M in additional revenue.
Data Sources and APIs
One of the things that makes Big Data powerful is its ability to pull multiple data sources together into a single analysis to produce unique insights. These disparate data sources include:
- Traditional structured data
- Internal documents
- Multimedia files
- Transaction data
- Social media data
- Sensor data
- Public web sources
- Machine log data
Many of these data sources can be accessed using application programming interfaces (APIs). These digital gateways enable Big Data storage and processing tools to access data sources in an automated way, freeing human analysts from the burden of trafficking data from source to target, and speeding time to insight.
We are living in truly remarkable times. The dramatic increase in the amount of data produced is nearly matched by the ability to collect, store and manage it. The challenge comes in applying smart analysis to, and generating actionable insight from, all that data.
What Is Big Data In Healthcare
Having an understanding of – what is Big Data – and the relationship between infrastructure, analytics, applications, data sources and APIs will help businesses to create a sustainable strategy to build their own Big Data ecosystem.
Contact Import.io to find out more about getting data for your business needs.
Data science is the study of data analyzing by advance technology (Machine Learning, Artificial Intelligence, Big data). It processes a huge amount of structured, semi-structured, unstructured data to extract insight meaning, from which one pattern can be designed that will be useful to take a decision for grabbing the new business opportunity, the betterment of product/service, ultimately business growth.
Data science process to make sense of Big data/huge amount of data that is used in business. The workflow of Data science is as below:
Data science process to make sense of Big data/huge amount of data that is used in business. The workflow of Data science is as below:
- Objective and the issue of business determining – What is organization objective, what level organization want to achieve at, what issue company is facing -these are the factors under consideration. Based on such factors which type of data are relevant is considered.
- Collection of relevant data- relevant data are collected from various source.
- Cleaning and filtering collected data – non-relevant data are removed.
- Explore the filtered, cleaned data – Finding any hidden pattern, synchronization in data, plotting them in the graph, chart, etc. form that is understandable to non-technical person.
- Creating a model by analyzing data – creating a model, validate it.
- Visualization of finding by interpreting data or created a model to a business person.
- Help businessperson in making the decision and taking the step for the sack of business growth.
Data Mining: It is a process of extracting insight meaning, hidden pattern from collected data that is useful to take a business decision in the purpose of decreasing expenditure and increasing revenue.
What Is Big Data Technology
Big Data: This is a term related to extracting meaningful data by analyzing the huge amount of complex, variously formatted data generated at high speed, that cannot be handled, processed by the traditional system.
What Is Big Data Technology
Data Expansion Day by Day: Day by day amount of data increasing exponentially because of today’s various data production sources like a smart electronic device. As per IDC (International Data Corporation) report, new data created per each person in the world per second by 2020 will be 1.7 MB. The amount of total data in the world by 2020 will reach around 44 ZettaBytes (44 trillion GigaByte) and 175 ZettaBytes by 2025. It is being seen that total volume of data being double every two years. Total size growth of data worldwide, year to year as per IDC report is shown below:
Source of Big Data:
- Social Media: Today’s world a good percent of the total world population is engaged with social media like Facebook, WhatsApp, Twitter, YouTube, Instagram, etc. Each activity on such media like uploading a photo, video, sending the message, making comment, putting like, etc create data.
- Sensor placed on the various place: Sensor placed in various place of the city that gathers data on temperature, humidity, etc. A camera placed beside road gather information about traffic condition, creates data. Security camera placed in a sensitive area like airport, railway station, shopping mall create a lot of data.
- Customer feedback on the product or service of the various company on their website creates data. For Example, a retail commercial site like Amazon, Walmart, Flipkart, Myntra gather customer feedback on the quality of their product, delivery time. Telecom company, other service provider organization seek customer experience with their service. These create a lot of data.
- IoT Appliance: Electronic devices that are connected to the internet create data for their smart functionality, examples are a smart TV, smart washing machine, smart coffee machine, smart AC, etc. It is machine-generated data that are created by sensor kept in various devices.
For Example, Smart printing machine – it is connected to the internet. A number of such printing machines connected to a network can transfer data within each other. So, if anyone loads a file copy in one printing machine, system store that file content, another printing machine kept in another building or another floor can print out that file hard copy. Such data transfer between various printing machines generates data. - In an e-commerce transaction, business transaction, banking, and the stock market, lots of records stored considered as one of the sources of big data. Payment through credit card, debit card or by another electronic way, all these are kept recorded as data.
- GPS in the vehicle that helps in monitoring movement of the vehicle to shorten the path for a destination to cut fuel, time consumption. This system creates huge data of vehicle position and movement.
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