What are the best methods for processing unstructured streaming data?

1 Data Ingestion

Data ingestion is the process of collecting, transferring, and loading streaming data from different sources to a destination system, such as a data lake, a data warehouse, or a cloud platform. Data ingestion can be done in batch or real-time mode, depending on the latency and throughput requirements of the application. Some of the tools and frameworks that can help with data ingestion are Apache Kafka, Apache Flume, Apache NiFi, Amazon Kinesis, and Google Cloud Pub/Sub.

2 Data Transformation

Data transformation is the process of converting, cleaning, enriching, and aggregating streaming data to make it suitable for analysis and consumption. Data transformation can be done either before or after loading the data to the destination system, depending on the complexity and frequency of the transformations. Some of the tools and frameworks that can help with data transformation are Apache Spark, Apache Flink, Apache Beam, Amazon Kinesis Data Analytics, and Google Cloud Dataflow.

3 Data Storage

Data storage is the process of storing and organizing streaming data in a way that enables efficient access and retrieval. Data storage can be done in different formats and systems, depending on the type and volume of the data, the query and analysis needs, and the cost and performance trade-offs. Some of the tools and frameworks that can help with data storage are Apache Hadoop, Apache Hive, Apache HBase, Apache Cassandra, Amazon S3, and Google Cloud Storage.

4 Data Analysis

Data analysis is the process of applying various techniques and methods to streaming data to extract meaningful information and insights. Data analysis can be done in different modes and levels, depending on the purpose and scope of the analysis, such as descriptive, diagnostic, predictive, or prescriptive. Some of the tools and frameworks that can help with data analysis are Apache Spark SQL, Apache Spark MLlib, Apache Flink SQL, Apache Flink ML, Amazon Kinesis Data Analytics SQL, and Google Cloud Dataflow SQL.

5 Data Visualization

Data visualization is the process of presenting and communicating streaming data in a graphical or interactive form. Data visualization can help with understanding and exploring streaming data, identifying patterns and trends, detecting anomalies and outliers, and making decisions and recommendations. Some of the tools and frameworks that can help with data visualization are Apache Zeppelin, Apache Superset, Grafana, Kibana, Amazon QuickSight, and Google Data Studio.

6 Data Quality

Data quality is the process of ensuring and maintaining the accuracy, completeness, consistency, timeliness, and reliability of streaming data. Data quality can affect the outcomes and value of streaming data processing, as well as the trust and confidence of the users and consumers. Some of the tools and frameworks that can help with data quality are Apache Nifi, Apache Atlas, Apache Griffin, Amazon Kinesis Data Quality, and Google Cloud Data Catalog.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?