Realtime Streaming

At realtimestreaming.dev, our mission is to provide a comprehensive resource for individuals and businesses seeking to understand and implement real-time data streaming processing. We strive to offer in-depth coverage of time series databases, as well as the latest developments in technologies such as Spark, Beam, Kafka, and Flink. Our goal is to empower our readers with the knowledge and tools they need to harness the power of real-time data streaming and drive innovation in their organizations.

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Real Time Streaming Cheatsheet

This cheatsheet is a reference guide for anyone getting started with real time data streaming processing, time series databases, Spark, Beam, Kafka, and Flink. It covers the basic concepts, topics, and categories related to these technologies.

Real Time Data Streaming Processing

Real time data streaming processing is the process of processing data in real time as it is generated. It involves the use of various technologies and techniques to process data as it is generated, rather than waiting for it to be stored in a database or other storage system.

Key Concepts

• Data Streaming: The process of generating and processing data in real time.
• Real Time Processing: The ability to process data as it is generated.
• Data Pipeline: The process of moving data from one system to another.
• Data Processing: The process of transforming data from one format to another.
• Data Analytics: The process of analyzing data to gain insights and make decisions.

Technologies

• Apache Kafka: A distributed streaming platform that allows you to publish and subscribe to streams of records.
• Apache Flink: A distributed processing engine for streaming data.
• Apache Spark: A distributed computing system for processing large datasets.
• Apache Beam: A unified programming model for batch and streaming data processing.

Time Series Databases

Time series databases are databases that are optimized for storing and querying time series data. They are designed to handle large volumes of data and provide fast, efficient access to that data.

Key Concepts

• Time Series Data: Data that is recorded over time.
• Data Points: Individual data values that are recorded over time.
• Time Series Database: A database that is optimized for storing and querying time series data.
• Data Retention: The length of time that data is stored in a time series database.
• Data Aggregation: The process of summarizing time series data.

Technologies

• InfluxDB: An open source time series database.
• TimescaleDB: An open source time series database built on top of PostgreSQL.
• OpenTSDB: An open source time series database built on top of HBase.

Apache Kafka

Apache Kafka is a distributed streaming platform that allows you to publish and subscribe to streams of records. It is designed to handle large volumes of data and provide fast, efficient access to that data.

Key Concepts

• Topics: A category or feed name to which records are published.
• Partitions: A topic can be divided into multiple partitions, each of which can be processed independently.
• Producers: Applications that publish records to Kafka topics.
• Consumers: Applications that subscribe to Kafka topics and process records.
• Brokers: Kafka servers that manage the storage and replication of records.

Commands

• Create a topic: bin/kafka-topics.sh --create --zookeeper localhost:2181 --replication-factor 1 --partitions 1 --topic my-topic
• List topics: bin/kafka-topics.sh --list --zookeeper localhost:2181
• Start a producer: bin/kafka-console-producer.sh --broker-list localhost:9092 --topic my-topic
• Start a consumer: bin/kafka-console-consumer.sh --bootstrap-server localhost:9092 --topic my-topic --from-beginning

Apache Flink

Apache Flink is a distributed processing engine for streaming data. It is designed to handle large volumes of data and provide fast, efficient processing of that data.

Key Concepts

• DataStreams: A stream of data that can be processed in real time.
• Operators: Functions that can be applied to DataStreams to transform or aggregate data.
• Windowing: The process of dividing a DataStream into windows and processing each window independently.
• Stateful Processing: The ability to maintain state across multiple events in a DataStream.

Commands

• Start a Flink cluster: ./bin/start-cluster.sh
• Submit a Flink job: ./bin/flink run <job-jar>

Apache Spark

Apache Spark is a distributed computing system for processing large datasets. It is designed to handle large volumes of data and provide fast, efficient processing of that data.

Key Concepts

• Resilient Distributed Datasets (RDDs): A distributed collection of data that can be processed in parallel.
• Transformations: Functions that can be applied to RDDs to transform or aggregate data.
• Actions: Functions that trigger the execution of transformations and return results.
• Spark SQL: A module for working with structured data using SQL.

Commands

• Start a Spark cluster: ./sbin/start-all.sh
• Submit a Spark job: ./bin/spark-submit <job-jar>

Apache Beam

Apache Beam is a unified programming model for batch and streaming data processing. It provides a simple, consistent API for processing data in both batch and streaming modes.

Key Concepts

• Pipelines: A sequence of data processing steps.
• Transforms: Functions that can be applied to data in a pipeline to transform or aggregate data.
• Sources: Input data for a pipeline.
• Sinks: Output data for a pipeline.

Commands

• Run a Beam pipeline: ./gradlew run -Pargs="--runner=DirectRunner"

Common Terms, Definitions and Jargon

1. Real-time data streaming: The process of continuously processing and analyzing data as it is generated in real-time.
2. Time series databases: A database designed to store and manage time-stamped data, such as sensor readings or stock prices.
3. Spark: An open-source distributed computing system designed for processing large-scale data sets.
4. Beam: An open-source unified programming model for batch and streaming data processing.
5. Kafka: An open-source distributed streaming platform used for building real-time data pipelines and streaming applications.
6. Flink: An open-source stream processing framework designed for high-throughput, low-latency data processing.
7. Data pipeline: A series of interconnected processes that move data from one system to another.
8. Data ingestion: The process of collecting and importing data from various sources into a data storage system.
9. Data processing: The manipulation and transformation of data to extract insights and value.
10. Data analytics: The process of examining data to uncover insights and trends.
11. Data visualization: The representation of data in a graphical or visual format to aid in understanding and analysis.
12. Data modeling: The process of creating a conceptual representation of data to facilitate analysis and decision-making.
13. Data architecture: The design and organization of data storage and processing systems.
14. Data governance: The management of data policies, standards, and procedures to ensure data quality, security, and compliance.
15. Data quality: The degree to which data is accurate, complete, and consistent.
16. Data security: The protection of data from unauthorized access, use, disclosure, or destruction.
17. Data privacy: The protection of personal and sensitive data from unauthorized access or use.
18. Data compliance: The adherence to legal and regulatory requirements related to data management and protection.
19. Data integration: The process of combining data from multiple sources into a unified view.
20. Data transformation: The process of converting data from one format or structure to another.

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