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Big Data Platforms and Ecosystems

Data Scientist Skillset_121321A
[Data Scientist Skillset]


Big Data, Big Opportunities


- Overview

Big data platforms are software frameworks that collect, store, process, and analyze large amounts of complex data. They are designed to handle the three V's of big data: volume, velocity, and variety. 

Big data platforms provide the following infrastructure, tools, and technologies: 

  • Distributed computing
  • Parallel processing
  • Responsive data architecture
  • Delivery at scale
  • AI-driven intelligent data management

Big data platforms have the following characteristics: 

  • Cloud native
  • Highly scaleable
  • Structured metadata
  • Support for ACID transactions
  • Modular (easy to change smaller components)

Examples of big data platforms include: Snowflake, BigQuery, Redshift, Databricks. 

Big data ecosystems, also known as "technology stacks", contain three fundamental elements: 

  • Responsive data architecture
  • Delivery at scale
  • AI-driven intelligent data management

Big Data ecosystems like Apache Spark, Apache Flink, and Cloudera Oryx 2 contain integrated ML libraries for large-scale data mining.

Data can be sourced from internal sources, such as databases, spreadsheets, CRMs, and other software. It can also be sourced from external sources, such as websites or third-party data aggregators. 


- Big Data Ecosystem

A data ecosystem refers to the combination of enterprise infrastructure and applications used to aggregate and analyze information. It enables organizations to better understand their customers and develop superior marketing, pricing and operational strategies.

Sometimes referred to as a “technology stack,” the modern big data ecosystem consists of three basic elements: responsive data architecture, delivery at scale, and intelligent data management driven by artificial intelligence.

A big data ecosystem refers to massive volumes of structured and unstructured data of a size or type that exceeds the capabilities of traditional relational databases. These are used to capture, manage and process data with low latency.

The big data ecosystem is like an ogre. Big data components are stacked in layers, building a stack. It's not as simple as taking data and turning it into insights. Big data analytics tools set a process through which raw data must go through to eventually generate information-driven actions in a company.

Data must first be acquired at source, translated and stored, then analyzed before finally being presented in an understandable format. This is a long and arduous process that can take months or even years to implement. But the payoff can be a game-changer: A solid big data workflow can be a huge differentiator for a business.


- Big Data Architecture

A Big Data Architecture is a framework that defines the components, processes, and technologies required to capture, store, process, and analyze Big Data. Big data architectures typically include four big data architecture layers: data collection and ingestion, data processing and analysis, data visualization and reporting, and data governance and security. Each layer has its own set of technologies, tools and processes.

Big data architectures are designed to handle the ingestion, processing, and analysis of data that is too large or complex for traditional database systems. The barriers to entry for organizations in the big data world vary based on the capabilities of users and their tools. For some, this can mean hundreds of gigabytes of data, for others hundreds of terabytes of data. As the tools for working with large data sets continue to advance, so does the meaning of big data. The term is increasingly referring to the value you can extract from data sets through advanced analytics, rather than strictly the size of the data, although in these cases they tend to be very large.

The data landscape has changed over the years. What you can or expect to do with your data has changed. The cost of storage has dropped dramatically, while the ways in which data is collected continue to grow. Some data comes quickly and needs to be continuously collected and observed. Other data arrives more slowly, but in very large chunks, often as decades-old data. You may be facing an advanced analytics problem, or one that requires machine learning. These are the challenges that big data architectures seek to address.


- Examples of Big Data Applications

Big data is a term used to describe data of great variety, volume, and velocity. In addition to its sheer volume, big data is so complex that traditional data management tools cannot store or process it efficiently. Data can be structured or unstructured. 

The technology known as Big Data is one of the most impactful innovations of the digital age. Patterns and correlations hidden in massive collections of data, revealed by powerful analytics, are informing planning and decision making across nearly every industry. In fact, within just the last decade, Big Data usage has grown to the point where it touches nearly every aspect of our lifestyles, shopping habits, and routine consumer choices.

Here are some examples of Big Data applications that affect people every day:

  • Mobile phone details
  • Social media content
  • Health records
  • Transaction data
  • Web search
  • Financial documents
  • Weather information
  • Transportation 

  • Education

  • Cybersecurity

  • Government

Big data can be generated by users (emails, images, transactional data, etc.) or machines (IoT, ML algorithms, etc.). Depending on the owner, the data can be made commercially available to the public via API or FTP. In some cases, you may need a subscription to gain access.


- Big Data Platforms

Due to the constant influx of data from numerous sources that will only become more intense, many sophisticated and highly scalable cloud data platforms are emerging to store and parse ever-expanding amounts of information. These types of platforms have become known as big data platforms.

A big data platform acts as an organized storage medium for large amounts of data. Big data platforms utilize a combination of data management hardware and software tools to store aggregated data sets, usually in the cloud.

How does Netflix or Spotify know exactly what you want to stream next? This is due in large part to the big data platforms working behind the scenes.

Understanding big data has become an asset in nearly every industry, from healthcare to retail and more. Companies increasingly rely on these platforms to collect vast amounts of data and turn it into disaggregated, actionable business decisions. This helps companies to better understand their customers, target audience, discover new markets and predict future steps.

Using an enterprise data platform not only provides a powerful business advantage, but is almost critical to keeping up with consumers, competing brands, and changing trends.


- Big Data, Structured, Unstructured and Semi-structured Data

Big data is defined as complex and large information sets, including structured, unstructured and semi-structured data sets, which are difficult to manage using traditional data processing tools. It requires additional infrastructure to manage, analyze and turn into insights.

Structured data refers to data that is in a standardized format, has a well-defined structure, conforms to a data model, follows a persistent order, and is easily accessible by humans and programs. This data type is usually stored in a database. 

Although structured data accounts for only about 20% of the world's data, it is the foundation of big data today. That's because it's easy to access, use, and the results of using it are far more accurate.

Semi-structured data refers to data that is not captured or formatted in a conventional way. Semi-structured data does not follow the format of a tabular data model or a relational database because it has no fixed schema. However, the data is not completely raw or unstructured, and does contain some structural elements, such as labels and organizational metadata, that make it easier to analyze. The advantage of semi-structured data is that it is more flexible and easier to scale than structured data.

In the modern big data world, unstructured data is most abundant. It's so prolific because unstructured data can be anything: media, images, audio, sensor data, text data, and more. Unstructured simply means that it is a data set (typically a large collection of files) that is not stored in a structured database format. 

Unstructured data has an internal structure, but it is not predefined through a data model. It may be human-generated or machine-generated in text or non-text format. 


- Big Data Systems

The traditional databases are not capable of handling unstructured data and high volumes of real-time datasets. Diverse datasets are unstructured lead to big data, and it is laborious to store, manage, process, analyze, visualize, and extract the useful insights from these datasets using traditional database approaches. However, many technical aspects exist in refining large heterogeneous datasets in the trend of big data. 

A big data system consists of the mandatory features Data, Data Storage, Information Management, Data Analysis, Data Processing, Interface and Visualization, and the optional feature, System Orchestrator. 

Key data-driven areas, including relational systems, distributed systems, graph systems, noSQL, newSQL, machine learning, and neural networks. Specifically, for example, areas include: cluster architecture, big Data stacks: Hadoop, Spark, Scheduling and Resource Management, batch and stream analytics, graph processing. serverless platforms, etc..


Big Data Systems-102022A
(Feature Model of Big Data Systems - ScienceDirect)

- The Role of Cloud Computing

Big data and cloud computing go hand-in-hand, with many public cloud services performing big data analytics. With Software as a Service (SaaS) becoming increasingly popular, keeping up-to-date with cloud infrastructure best practices and the types of data that can be stored in large quantities is crucial.  

Cloud computing is the delivery of computing services like servers, storages and more over the Internet. The companies that offer these computing services are called cloud providers. They charge for cloud computing services based on usage.

Cloud computing is usually classified on the basis of location, or on the service that the cloud is offering. Based on a cloud location, we can classify cloud as: Public, Private, Hybrid, and Community Cloud. Based on a service that the cloud is offering, we classify as: IaaS (Infrastructure-as-a-Service), PaaS(Platform-as-a-Service), SaaS(Software-as-a-Service), or, Storage, Database, Information, Process, Application, Integration, Security, Management, Testing-as-a-service.

Although you do not realize you are probably using cloud computing right now, most of us use an online service to send email, edit documents, watch movies, etc. It is likely that cloud computing is making it all possible behind the scenes. 


- Big Data Integration

Big data integration describes the connection between data management operations and the big data processing patterns needed to utilize them in large-scale analytical applications.

Data integration is now a practice in all organizations. Data needs to be protected, governed, transformed, usable, and agile. Data supports everything that we do personally and supports organizations’ ability to deliver products and services to us. Whatever your big data application is, and the types of big data you are using the real value will come from integrating different types of data sources, and analyzing them at scale. 

Data integration means bringing together data from diverse sources and turning them into coherent and more useful information (or knowledge). The main objective here is taming or more technically managing data and turning it into something you can make use of programmatically. A data integration process involves many parts. It starts with discovering, accessing, and monitoring data and continues with modeling and transforming data from a variety of sources. Moreover, integration of diverse datasets significantly reduces the overall data complexity. The data becomes more available for use and unified as a system of its own. Such a streamlined and integrated data system can increase the collaboration between different parts of your data systems. Each part can now clearly see how their data is integrated into the overall system, including the user scenarios and the security and privacy processes around it. 


 - Big Data Tools and Techniques

Nowadays, large volume of data is generated in the form of text, voice, video, images and sound. It is very challenging job to handle and to get process these different types of data. It is very laborious process to analysis big data by using the traditional data processing applications. Due to huge scattered file systems, a big data analysis is a difficult task. So, to analyses the big data, a number of tools and techniques are required. 

When it comes to managing large amounts of data and performing complex operations on these massive amounts of data, big data tools and techniques are a must. The big data ecosystem and its domain is what we mean when we talk about using big data tools and techniques. There is no solution provided for every use case and needs and must be created and crafted in an efficient manner according to company needs. Big data solutions must be developed and maintained according to the needs of the company to meet the needs of the company. Can build and maintain stable big data solutions, making them available for requested problems.

Some of the techniques of data mining are used to analyze the big data such as clustering, prediction, and classification and decision tree etc. Apache Hadoop, Apache spark, Apache Storm, MongoDB, NOSQL, HPCC are the tools used to handle big data.


- Building a Big Data Team and Strategy

In reality, a data scientist is a group of people who act in unison. Data science teams often come together to analyze situations, business or scientific cases that cannot be solved individually. The solution has many moving parts. But ultimately, all these pieces should come together to provide actionable insights based on big data. Being able to use evidence-based insights in business decisions is now more important than ever. Data scientists combine technical, business and soft skills to achieve this. 

When building a big data strategy, it is important to align big data analytics with business goals. Communicate goals and provide organizational support for analysis projects. Build a diverse talent team and establish team spirit. Remove barriers to data access and integration. Ultimately, these activities need to be iterative in response to new business goals and technological advancements. 

Often, in large enterprises, most of their data used to run in silos. Keeping data in disparate systems forces their teams to make siloed decisions. While this approach is a common result of organic growth over time, connecting the pieces and optimizing the entire data asset can be difficult. In turn, applying advanced analytics and machine learning has become more difficult, and deeper insights remain out of reach. 

However, it is no longer necessary to group data into business groups and use it individually for internal business applications. Instead, the modern data age requires a well-curated strategic infrastructure to deliver on the promise of deep, transformative insights. 

Modernizing data assets isn't always easy. It involves introducing new processes, using new tools, and people who support cultural change.


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