Analytics Flow for Big Data, Big Data Stack

Analytics Flow for Big Data

The analytics flow for big data refers to the process of collecting, storing, processing, and analyzing large and complex data sets to gain insights and make better decisions. It typically includes the following steps:

1. Data collection: Data is collected from various sources such as social media, IoT devices, and sensors. The data can be structured, semi-structured, or unstructured and may need to be cleaned and transformed before it can be analyzed.
2. Data storage: The data is stored in a centralized repository such as a data lake, Hadoop Distributed File System (HDFS), or NoSQL database.
3. Data processing: The data is processed using technologies such as Hadoop MapReduce, stream processing, and machine learning to extract insights and prepare it for analysis.
4. Data analysis: The data is analyzed using tools such as SQL, data visualization, and machine learning algorithms to gain insights and make better decisions.
5. Data governance: Data governance policies and procedures are put in place to ensure data is accurate, complete, consistent and compliant with regulations.
6. Data security: Security measures such as data encryption, access controls, and incident response are implemented to protect sensitive information and prevent unauthorized access.
7. Data visualization: The data is transformed into interactive and easy-to-understand visualizations using tools such as Tableau, QlikView and Power BI.
8. Decision-making: Insights from the data are used to make better decisions and take action.

This flow is not a one-time process, it is an ongoing cycle that involves continuous collection, processing, and analysis of data. As new data is collected, it is added to the data lake, processed, and analyzed to gain new insights and improve decision-making.

Big data analytics also involves collaboration between different teams such as data engineers, data scientists, data analysts, and business analysts. Each team plays a different role in the analytics flow, but they all work together to gain insights from the data and make better decisions.

Big Data Stack

The big data stack refers to the combination of technologies and tools that are used to collect, store, process, and analyze large and complex data sets. It typically includes the following layers:

1. Data Ingestion: This layer is responsible for collecting data from various sources such as social media, IoT devices, and sensors. Technologies such as Apache Kafka, Apache NiFi, and Apache Storm are commonly used for data ingestion.
2. Data Storage: This layer is responsible for storing the data in a centralized repository such as a data lake, Hadoop Distributed File System (HDFS), or NoSQL database. Technologies such as Apache Hadoop, Apache Cassandra, and MongoDB are commonly used for data storage.
3. Data Processing: This layer is responsible for processing the data using technologies such as Hadoop MapReduce, Apache Spark, and Apache Storm. This layer also includes machine learning libraries like Apache Mahout and MLlib.
4. Data Analysis: This layer is responsible for analyzing the data using tools such as SQL, data visualization, and machine learning algorithms. Technologies such as Apache Hive, Apache Pig, and Apache Impala are commonly used for SQL-based analysis, while tools such as Tableau, QlikView, and Power BI are commonly used for data visualization.
5. Data Governance: This layer is responsible for data governance policies and procedures to ensure data is accurate, complete, consistent and compliant with regulations. Technologies like Apache Atlas, Apache Ranger, and Apache Sentry are commonly used for data governance.
6. Data Security: This layer is responsible for implementing security measures such as data encryption, access controls, and incident response to protect sensitive information and prevent unauthorized access. Technologies such as Apache Ranger, Apache Knox, and Apache Sentry are commonly used for data security.
7. Data Visualization: This layer is responsible for transforming the data into interactive and easy-to-understand visualizations using tools such as Tableau, QlikView, and Power BI.
8. Operationalization: This layer is responsible for deploying and managing the big data stack in production environments. Technologies such as Apache Ambari, Apache ZooKeeper, and Apache Mesos are commonly used for operationalization.

The big data stack is constantly evolving, with new technologies and tools being developed to handle the volume, variety, and velocity of big data. The stack can be different from organization to organization and it depends on the specific use case and the data being handled.

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