Big Data Platforms and Ecosystems
- [Data Scientist Skillset]
Big Data, Big Opportunities
- Overview
A big data platform and ecosystem refers to a comprehensive system of interconnected tools and technologies designed to collect, store, process, and analyze large volumes of data (big data), typically involving multiple software applications working together to manage and extract insights from diverse data sources, including structured, semi-structured, and unstructured data.
Essentially, it's a complete infrastructure for handling big data analysis, with different components like data ingestion, storage, processing engines, and visualization tools all working together seamlessly.
A big data ecosystem refers to a comprehensive system of various functional components and enabling tools that are used to harness the capabilities of big data. It involves not only computing and storing big data, but also leveraging the advantages of a systematic platform and the potential of big data analytics.
Key features about big data platforms and ecosystems:
- Functional Components: A typical big data ecosystem includes data ingestion tools, distributed storage systems (like Hadoop Distributed File System), data processing engines (like Apache Spark), data warehousing solutions, data visualization tools, and data governance mechanisms.
- Scalability: Big data platforms are designed to handle massive data volumes and can scale horizontally by adding more processing nodes to a cluster as data needs increase.
- Variety of Data: They can manage diverse data types like text, images, videos, sensor data, and structured database records.
- Open-Source Prevalence: Many popular big data platforms are open-source, like Apache Hadoop, Apache Spark, and Apache Pig, allowing flexibility and customization.
- 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 customers and develop superior marketing, pricing, and operational strategies.
Sometimes referred to as a “technology stack,” the modern big data ecosystem consists of three essential elements: responsive data architecture, delivery at scale, and AI-driven smart data management.
A big data ecosystem refers to large amounts of structured and unstructured data whose size or type exceeds the capabilities of traditional relational databases. They are used to capture, manage, and process data with low latency.
A big data ecosystem is like an ogre. Big data components are layered on top of each other to build the stack. It’s not as simple as taking data and turning it into insights. Big data analytics tools establish a process that raw data must go through to ultimately produce information-driven actions in your company.
Data must first be acquired from the source, then translated and stored, then analyzed, and finally presented in an understandable format. It’s a long, arduous process that can take months or even years to achieve. But the payoff can be game-changing: A reliable big data workflow can be a huge differentiator for your business.
- Big Data Architecture
Big data architecture is a framework that allows organizations to ingest, store, process, and analyze data that is too large or complex for traditional database systems. Big data is unique because of its volume, variety, and velocity. These factors make it difficult for traditional computer databases to handle big data sets.
Big data architectures are designed by data architects and take into account the organization's unique needs, structure, and data sources. They can include multiple layers or components, such as:
- Real-time streaming data inputs: Such as IoT devices
- Data storage: Stores data and converts unstructured data into a format that analytic tools can use
- Batch processing: Long-running batch jobs that filter, combine, and render data usable for analysis
A well-designed big data architecture can help organizations save money and make better business decisions.
- Big Data Platforms
A big data platform is a computing solution that manages and processes large amounts of data using software, hardware, and tools. Big data platforms help businesses use data for strategic purposes and gain a competitive advantage.
Big data platforms can: Store data, Process data, Analyze data, and Visualize data.
Some examples of big data platforms include:
- Apache Hadoop: An open-source framework that allows for distributed processing of large datasets. Hadoop uses a distributed file system to store data across multiple machines, which improves fault tolerance and availability.
- Cloudera: A software company that provides a platform for analyzing big data using Apache Hadoop. Cloudera was created by engineers from Facebook, Google, Oracle, and Yahoo.
- Data lakes: A storage environment for raw data that can store both structured and unstructured data. Data lakes are often built on the Hadoop ecosystem, and many have moved to the cloud.
Other big data platforms include: Apache Spark, Amazon Redshift, Google BigQuery, Microsoft Azure HDInsight, and Databricks.
- Big Data, Structured, Unstructured and Semi-structured Data
Big data refers to extremely large and diverse datasets that are difficult to manage and process using traditional database systems, encompassing both structured, semi-structured, and unstructured data; while "structured data" is highly organized and fits neatly into a defined format like a database table, "unstructured data" lacks a predefined structure and is often raw like text or images, and "semi-structured data" sits between the two, having some organization but not following a strict schema, like data in XML or JSON files.
Key points about each data type:
Structured Data:
- Well-organized with clear defined fields and relationships.
- Easily stored and queried in relational databases.
- Examples: Customer details in a CRM system, spreadsheet data.
Semi-Structured Data:
- Has some level of organization but not as rigid as structured data.
- Uses tags or markers to identify data elements.
- Examples: XML files, JSON documents, log files.
Unstructured Data:
- No predefined format or structure.
- Can be text, images, audio, video, social media posts.
- Requires advanced techniques to extract meaningful information.
Structured data is stored in a predefined format and is highly specific; whereas unstructured data is a collection of many varied data types that are stored in their native formats; while semi-structured data does not follow the tabular data structure models associated with relational databases or other data table
- (Feature Model of Big Data Systems - ScienceDirect)
- Big Data Systems
A Big Data System is defined by its ability to process massive volumes of diverse data at high speed, requiring resources from multiple computers to handle the workload efficiently.
Big data systems are tools and technologies that help organizations process and store large and complex data sets. They are designed to handle the volume, variety, and velocity of data, which can come from a variety of sources like web, mobile, email, and social media.
Big data systems are used to:
- Integrate data: Combine data from different sources into a single structure
- Process data: Clean, organize, and prepare data to remove errors and redundancy
- Manage and store data: Ensure data is stored efficiently so it can be used when needed
Big data systems are often used in machine learning (ML) projects, predictive modeling, and other advanced analytics applications. They can also be combined with other technologies like the Internet of Things (IoT), ML, and artificial intelligence (AI) to handle data in real time.
Some examples of big data systems include: Hadoop, MongoDB, Amazon Web Services (AWS), Google Cloud Platform (GCP), Microsoft Azure.
- Big Data Integration and Processes
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.
- Convergence of Big Data and Cloud Computing
The convergence of big data and cloud computing has changed the way enterprises process, analyze, and leverage data. The two technologies have a mutually beneficial relationship: big data requires huge storage capacity and powerful computing power for analysis, both of which are the specialties of cloud computing platforms.
Cloud solutions offer scalable storage options and provide powerful computing power to process and analyze data, making them an ideal platform for big data management. These services enable organizations to quickly deploy big data applications without worrying about the underlying infrastructure.
For example, cloud computing solutions are designed for scalability and high performance, making them ideal for handling big data tasks.
The cloud platform allows enterprises to deploy applications with a single click, run workloads on high-performance virtual machines (VMs), and enjoy seamless collaboration across teams. American Cloud also provides a compelling API for ultra-fast queries and real-time metrics monitoring of massive data sets.
The combination of big data and cloud computing results in scalable, cost-effective, and efficient solutions. The combined use of these technologies enhances data storage capabilities and promotes faster analysis and decision-making, thereby improving overall productivity and performance.
The cloud plays the role of an enabler, providing a hosting platform for big data, just as it does for the Internet of Things (IoT). This allows for the creation of complex yet harmonious technology ecosystems that drive business innovation.
