13.2 Big Data Technologies and Architectures
5 min read•july 30, 2024
Big Data Technologies and Architectures are crucial for handling massive datasets. From and Spark to NoSQL databases, these tools enable processing, storage, and analysis of structured and unstructured data at scale.
Distributed computing, batch vs real-time processing, and thoughtful architecture design are key concepts. Understanding these technologies and approaches helps organizations extract valuable insights from their data, driving informed decision-making and innovation.
Big Data Technologies and Tools
Core Big Data Frameworks and Platforms
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- Hadoop processes and stores large volumes of structured, semi-structured, and unstructured data
- Consists of components like (Hadoop Distributed File System) for storage
- Uses for distributed processing
- performs fast, in-memory data processing
- Supports batch processing, real-time streaming, machine learning, and graph processing
- Provides APIs for Java, Scala, Python, and R
- NoSQL databases handle large-scale, unstructured data
- Document-oriented databases store data in flexible, JSON-like documents (MongoDB)
- Column-oriented databases optimize for queries over large datasets (Cassandra)
- Graph databases efficiently store and query highly connected data (Neo4j)
Data Processing and Analytics Tools
- Stream processing technologies enable real-time data ingestion and analysis
- functions as a distributed messaging system for high-throughput data streams
- processes unbounded and bounded data streams at scale
- Machine learning libraries implement advanced analytics and predictive modeling
- builds and trains neural networks for deep learning applications
- provides dynamic computational graphs for flexible model development
- offers a wide range of algorithms for classification, regression, and clustering
- Data visualization tools present insights in easily understandable formats
- creates interactive and data stories
- integrates with Microsoft products for business intelligence reporting
- builds custom, web-based data visualizations using JavaScript
Distributed Computing for Big Data
Fundamentals of Distributed Computing
- Distributed computing divides large computational tasks across multiple networked computers
- Improves processing efficiency and speed for big data workloads
- Enables horizontal scaling by adding more machines to the cluster
- MapReduce programming model facilitates parallel processing of data
- Map phase distributes data and computations across nodes
- Reduce phase aggregates results from individual nodes
- Distributed file systems store and retrieve large datasets across multiple machines
- HDFS (Hadoop Distributed File System) provides fault tolerance through data replication
- (GFS) inspired the development of HDFS
Resource Management and Task Scheduling
- Cluster management systems allocate resources and schedule tasks
- (Yet Another Resource Negotiator) manages resources in Hadoop clusters
- orchestrates containerized applications across distributed environments
- Load balancing techniques ensure even distribution of workloads
- Round-robin scheduling assigns tasks to nodes in a circular order
- Least connection method directs new tasks to the node with the fewest active connections
- Fault tolerance mechanisms maintain system reliability
- Data replication creates multiple copies of data across different nodes
- Task reallocation reassigns failed tasks to healthy nodes in the cluster
Batch vs Real-Time Data Processing
Characteristics of Batch Processing
- Batch processing collects and processes data in large, discrete groups
- Suited for complex analytics on large volumes of historical data
- Typically runs at scheduled intervals (daily, weekly, monthly)
- Advantages of batch processing include:
- Ability to handle very large datasets efficiently
- Comprehensive analysis of complete datasets
- Lower operational costs due to scheduled resource usage
- Common batch processing technologies:
- Hadoop MapReduce for distributed batch processing
- for SQL-like querying of large datasets
- for high-level data flow scripting
Real-Time Processing Fundamentals
- Real-time processing continuously ingests and analyzes data as it's generated
- Provides immediate insights and actions on incoming data
- Ideal for time-sensitive applications requiring low-latency results
- Advantages of real-time processing include:
- Immediate response to changing conditions or events
- Ability to detect and respond to patterns or anomalies in real-time
- Support for interactive applications and live dashboards
- Popular real-time processing technologies:
- Apache Kafka for high-throughput, fault-tolerant messaging
- Apache Flink for stateful computations over data streams
- for distributed real-time computation
Hybrid Approaches and Considerations
- Lambda architecture combines batch and real-time processing
- Batch layer processes historical data for comprehensive views
- Speed layer handles real-time data for immediate insights
- Serving layer combines results from both layers for query responses
- Factors influencing the choice between batch and real-time processing:
- Data volume and velocity requirements
- Business needs for data freshness and latency
- Complexity of analytics and computations required
- Available infrastructure and resources
Big Data Architecture Design
Data Ingestion and Storage Layer
- Data ingestion layer collects and imports data from various sources
- Apache Kafka ingests real-time streaming data from multiple producers
- Apache Flume collects, aggregates, and moves large amounts of log data
- Apache Sqoop transfers data between Hadoop and relational databases
- Data storage layer selects appropriate solutions based on data types and access patterns
- HDFS provides large-scale distributed storage for unstructured data
- offers column-oriented storage for semi-structured data
- serves as a scalable object storage system for cloud-based architectures
Data Processing and Analytics Layer
- Data processing layer incorporates technologies for transformation, analysis, and modeling
- Apache Spark performs in-memory processing for batch and stream data
- Apache Flink enables stateful computations over data streams
- Apache Drill provides SQL query engine for various data sources
- Analytics and machine learning components support advanced data analysis
- offers scalable machine learning algorithms
- provides an open-source machine learning platform
- enables interactive data analytics with notebook interfaces
Data Visualization and Consumption Layer
- Data visualization layer presents insights and makes data accessible to end-users
- Tableau creates interactive dashboards and reports
- offers a modern, enterprise-ready business intelligence web application
- visualizes time series data for monitoring and observability
- API and service layer exposes data and analytics results to applications
- RESTful APIs provide programmatic access to processed data
- GraphQL enables flexible querying of data from multiple sources
- Apache Kafka Connect integrates streaming data with external systems
Key Terms to Review (39)
Agile analytics: Agile analytics refers to a flexible and iterative approach to data analysis that emphasizes rapid development and continuous improvement in decision-making processes. It integrates techniques from agile software development, allowing organizations to adapt quickly to changing data requirements and business environments, fostering collaboration among cross-functional teams.
Amazon S3: Amazon S3 (Simple Storage Service) is a scalable object storage service designed for storing and retrieving any amount of data from anywhere on the web. It's built to handle big data workloads and is widely used in big data technologies due to its durability, availability, and security features, making it an integral part of data architectures.
Apache Flink: Apache Flink is an open-source stream processing framework designed for real-time data processing and analytics. It allows users to process large volumes of data with low latency and high throughput, making it ideal for applications that require immediate insights from streaming data sources.
Apache HBase: Apache HBase is a distributed, scalable, NoSQL database built on top of the Hadoop ecosystem. It is designed to handle large amounts of data across many servers while providing real-time access to that data, making it ideal for applications that require fast read and write capabilities. HBase is modeled after Google Bigtable and supports sparse data sets, which allows it to efficiently store massive amounts of structured and semi-structured data.
Apache Hive: Apache Hive is a data warehouse software built on top of Hadoop that facilitates reading, writing, and managing large datasets residing in distributed storage using a SQL-like interface. It allows users to query and analyze data in Hadoop through a familiar structure, making it easier to work with Big Data technologies without requiring extensive programming skills.
Apache Kafka: Apache Kafka is an open-source distributed event streaming platform designed for high-throughput, fault-tolerant data handling in real time. It allows applications to publish and subscribe to streams of records, making it an essential tool for building real-time data pipelines and streaming applications. Its ability to process large volumes of data quickly connects it closely with big data technologies and programming analytics.
Apache Mahout: Apache Mahout is an open-source project designed to create scalable machine learning algorithms for big data processing. It is primarily used for clustering, classification, and recommendation tasks, making it a valuable tool in the landscape of big data technologies. By leveraging distributed computing frameworks like Apache Hadoop, Mahout allows users to analyze large datasets efficiently and derive meaningful insights.
Apache Pig: Apache Pig is a high-level platform for creating programs that run on Apache Hadoop, a framework used for processing large data sets in a distributed computing environment. It provides a simple language called Pig Latin for data analysis, enabling users to write complex data transformations without needing to know Java, the underlying language of Hadoop. By simplifying the process of working with big data, Apache Pig enhances productivity and helps users focus on data processing rather than the intricacies of the programming environment.
Apache Spark: Apache Spark is an open-source, distributed computing system designed for fast processing of large-scale data. It provides an interface for programming entire clusters with implicit data parallelism and fault tolerance, making it a go-to choice for big data analytics. Its ability to process data in-memory significantly speeds up data retrieval and computation compared to traditional systems like Hadoop MapReduce.
Apache Storm: Apache Storm is a distributed real-time computation system that allows for processing streams of data in a fault-tolerant way. It enables users to process data continuously, making it ideal for applications that require real-time analytics and decision-making, such as monitoring social media feeds or financial transactions. Storm's ability to handle large volumes of data with low latency makes it a key player in the landscape of big data technologies.
Apache Superset: Apache Superset is an open-source data visualization and business intelligence platform that enables users to create interactive dashboards and explore data sets with ease. It provides a user-friendly interface that allows individuals to connect to various data sources, perform data analysis, and visualize insights without extensive programming knowledge. This makes it a powerful tool for organizations looking to leverage big data technologies and architectures to make informed decisions based on data-driven insights.
Apache YARN: Apache YARN (Yet Another Resource Negotiator) is a resource management layer for Hadoop that allows multiple data processing engines to handle data stored in a single platform. It enhances the Hadoop ecosystem by enabling better resource utilization and scheduling for various applications, thus improving efficiency in processing big data workloads.
Apache Zeppelin: Apache Zeppelin is an open-source web-based notebook that enables interactive data analytics and collaborative data science. It allows users to create and share documents that contain live code, equations, visualizations, and narrative text, making it easier to analyze big data using various backend systems like Apache Spark, Flink, and others. By providing a platform for combining data visualization with coding, Zeppelin enhances the ability to explore and present data-driven insights effectively.
Big Data Engineer: A big data engineer is a specialized professional responsible for designing, building, and maintaining the infrastructure and architecture that allows for the processing and analysis of large datasets. This role encompasses the development of data pipelines, ensuring data quality, and optimizing data storage solutions to facilitate efficient data retrieval and analysis. Big data engineers work closely with data scientists and analysts to ensure that the data is readily accessible and structured properly for use in various applications.
Cloud computing: Cloud computing is the delivery of computing services, including storage, processing power, and applications, over the internet. This technology allows businesses and individuals to access and utilize resources without needing physical infrastructure, making it scalable, cost-effective, and flexible. It plays a crucial role in data management, analytics, and connectivity in various applications.
Crisp-DM: Crisp-DM stands for Cross-Industry Standard Process for Data Mining, which is a structured framework designed to guide data mining and analytics projects from inception to completion. This methodology emphasizes an iterative process, allowing teams to refine their analyses and models continuously. By providing a clear roadmap, Crisp-DM helps organizations tackle the challenges of big data by ensuring that they effectively understand and utilize the data at hand.
D3.js: d3.js is a powerful JavaScript library used for producing dynamic, interactive data visualizations in web browsers. By leveraging web standards such as HTML, SVG, and CSS, d3.js enables developers to bind data to a Document Object Model (DOM), facilitating the creation of complex visual representations of data sets. Its versatility allows it to be integrated with various big data technologies, making it an essential tool for effective storytelling through data.
Dashboards: Dashboards are visual displays of key performance indicators (KPIs) and relevant data that provide a quick overview of performance metrics and trends within a business context. They serve as an essential tool in business analytics by summarizing complex data sets into intuitive visual formats, allowing stakeholders to monitor progress and make informed decisions. Dashboards often integrate data from multiple sources, making it easier to identify patterns, anomalies, and actionable insights.
Data Governance: Data governance is the framework for managing data assets within an organization, ensuring that data is accurate, available, secure, and compliant with regulations. It involves establishing policies, procedures, and responsibilities for data management, which is critical to maintaining data quality and integrity in business analytics, driving informed decision-making, and navigating the complexities of big data technologies.
Data lake: A data lake is a centralized repository that allows for the storage of vast amounts of raw data in its native format until it is needed for analysis. Unlike traditional data warehouses, which store structured data in predefined schemas, data lakes accommodate a wide variety of data types, including structured, semi-structured, and unstructured data, making them highly flexible for big data analytics and advanced processing techniques.
Data mining: Data mining is the process of discovering patterns, correlations, and insights from large sets of data using statistical and computational techniques. This method helps organizations transform raw data into meaningful information, enabling better decision-making across various applications such as customer behavior analysis, predictive modeling, and trend identification.
Data quality management: Data quality management refers to the processes and techniques used to ensure that data is accurate, consistent, and reliable. It involves various practices aimed at maintaining and improving the quality of data throughout its lifecycle, which is crucial when dealing with large volumes of data from diverse sources in big data environments. Good data quality management helps organizations make better decisions, improves operational efficiency, and supports compliance with regulations.
Data scientist: A data scientist is a professional who uses statistical methods, algorithms, and programming skills to analyze complex data sets and derive meaningful insights. They play a crucial role in transforming raw data into actionable intelligence, often leveraging big data technologies and architectures to process and analyze vast amounts of information efficiently.
Data warehousing: Data warehousing is the process of collecting, storing, and managing large volumes of data from various sources to support business intelligence activities, reporting, and analytics. It serves as a centralized repository where data is consolidated, organized, and made accessible for analysis, helping organizations make informed decisions based on historical and current data insights.
Google File System: The Google File System (GFS) is a proprietary distributed file system developed by Google to manage large amounts of data across multiple machines. It is designed for high fault tolerance and provides efficient access to large data sets, making it a crucial component in handling big data workloads within Google's infrastructure.
Grafana: Grafana is an open-source data visualization and monitoring platform that enables users to create interactive and dynamic dashboards. It connects with various data sources, including databases and cloud services, allowing for real-time data exploration and analysis. Grafana's flexibility and ease of use make it a popular tool for monitoring applications, infrastructure, and business metrics in the context of big data technologies and architectures.
H2o.ai: h2o.ai is an open-source software platform designed for machine learning and big data analytics. It allows users to build predictive models quickly and efficiently using algorithms like generalized linear models, gradient boosting machines, and deep learning. The platform integrates with various big data technologies, making it suitable for handling large datasets and enabling organizations to derive insights and make data-driven decisions.
Hadoop: Hadoop is an open-source framework designed for distributed storage and processing of large datasets using clusters of computers. It enables organizations to efficiently handle big data by allowing them to store vast amounts of information across multiple machines and process it in parallel, which helps overcome the limitations of traditional data processing systems.
HDFS: Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware, providing high-throughput access to application data. It is a key component of the Hadoop ecosystem, enabling the storage and processing of large datasets across multiple machines while ensuring reliability and fault tolerance through data replication.
Heatmaps: Heatmaps are graphical representations of data where individual values are represented in colors, allowing for quick visual analysis of information. They help identify patterns, correlations, and trends within datasets, making them valuable tools for data exploration and interpretation. By using color gradients to show varying levels of intensity or frequency, heatmaps can simplify complex data and highlight areas of interest.
Kubernetes: Kubernetes is an open-source platform designed to automate the deployment, scaling, and management of containerized applications. It provides a framework to run distributed systems resiliently, allowing users to manage their applications efficiently across various environments, whether on-premises or in the cloud.
Mapreduce: MapReduce is a programming model and processing technique used for handling large datasets across distributed computing environments. It allows for the efficient processing of big data by breaking down tasks into smaller, manageable parts through a two-step process: the 'Map' function that sorts and filters data, and the 'Reduce' function that aggregates and summarizes the processed data. This model is crucial in the context of big data technologies and architectures, enabling scalability and fault tolerance in data processing.
NoSQL Database: A NoSQL database is a type of database designed to store and manage large volumes of unstructured or semi-structured data, enabling high scalability and flexibility. Unlike traditional relational databases that rely on fixed schemas and SQL for data manipulation, NoSQL databases use various data models such as key-value, document, column-family, or graph to efficiently handle diverse data types and structures.
Power BI: Power BI is a powerful business analytics tool developed by Microsoft that enables users to visualize data and share insights across their organization, or embed them in an app or website. It connects to a variety of data sources, transforming raw data into interactive reports and dashboards that help drive decision-making and business strategy.
Predictive Analytics: Predictive analytics involves using statistical techniques and machine learning algorithms to analyze historical data and make predictions about future outcomes. By identifying patterns and trends in data, it helps organizations anticipate future events, enabling proactive decision-making and strategy formulation.
PyTorch: PyTorch is an open-source machine learning library used for applications such as deep learning and artificial intelligence. It offers a flexible platform for building and training neural networks, allowing developers to create dynamic computational graphs and optimize models with ease. This adaptability makes it a popular choice among researchers and practitioners in the big data landscape.
Scikit-learn: scikit-learn is a popular open-source machine learning library for Python that provides simple and efficient tools for data mining and data analysis. It supports various supervised and unsupervised learning algorithms, making it an essential tool for building predictive models and conducting data analysis in a variety of fields, including business applications. With its user-friendly interface and extensive documentation, scikit-learn enables users to easily implement machine learning techniques, fostering innovation and efficiency in data-driven decision-making.
Tableau: Tableau is a powerful data visualization tool that helps users create interactive and shareable dashboards. It allows businesses to visualize their data in a way that facilitates understanding and insight, making it a popular choice for data analysis and decision-making processes.
Tensorflow: TensorFlow is an open-source machine learning framework developed by Google that enables the building and training of machine learning models. It provides a flexible platform for both researchers and developers to create complex algorithms, particularly those involving neural networks, making it a key player in big data technologies and the application of machine learning in various industries.