October 29th, 2020
To meet the changing needs of modern-day business and market requirements, organizations are undergoing digital transformations and have started to evaluate their data as digital assets and employ strategies to utilize them to extract maximum output efficiently. The process often involves collecting data from various sources, cleaning, restructuring, enriching it into a more usable format, and finally storing it in a data lake or data-warehouse to be consumed by data analytics toolkits. By implementing a robust and scalable data pipeline solution, organizations are able to streamline data collection, data structuring, data cleaning, data validation, data enrichment, and data provisioning.
Water consumed by everyday households is served through well-connected and reliable pipelines that are responsible for supplying water from various natural sources like rivers, ponds, etc., and then filtering and treating it with necessary methods to make it ready for human consumption. Prior to the advent of water pipelines, people had to collect water from the sources and run the water through a treatment process themselves to make it ready for consumption. The creation of water pipelines eased the process. Consumers no longer needed to worry about the source and the filtration process and are now only concerned about the amount of usage of the water delivered through the pipeline.
A water pipeline is the same principle behind the concept of the data pipeline. A data pipeline is a logical arrangement to transport data from source to data consumer, facilitating processing or transformation of data during the movement. The transportation of data from any source to a destination is known as the data flow. The arrangement of software and tools that form the series of steps to create a reliable and efficient data flow with the ability to add intermediary steps to process or transform the data is known as a data pipeline. Generally, data pipelines consist of three key elements:
Most commonly employed data pipelines involve automatically collecting data from many disparate sources, then transforming and finally consolidating it into a data warehouse or a high-performance polyglot persistence.
Based on usage pattern, data pipelines are classified into the following types:
Batch: This type of data pipeline is useful when the requirements involve processing and moving large volumes of data at a regular interval. With a batch data pipeline, the data is periodically collected, transformed, and processed in blocks (batches) and finally moved to the destination. For example, loading marketing data into a larger system for analysis by using computationally intensive operational solutions like Hadoop, Spark, etc.
Real-Time: This type of data pipeline is useful when the requirements involve the optimized processing of data in near real-time. These solutions support collecting data from a streaming source and processing chunks of data streams instead of processing batches of datasets. Unlike batch data pipelines, this involves ingesting a sequence of data and progressively updating metrics, reports, and summary statistics in response to the continually flowing data records.
ETL (Extract, Transform and Load) is a process to extract data from one system, transform and load it into a storage warehouse. Almost all traditional ETL pipelines typically run in batches where the data is moved in one large chunk at a specific time to the target system. On the other hand, a data pipeline refers to an arrangement of systems or processes for moving data from one system to another where the data may or may not be transformed during transit. Also, the data may be processed in real-time instead of batches. Additionally, data may or may not be loaded into a data warehouse or a database in general. Thus, the data pipeline is a broader term that encompasses ETL as a subset depending on the type of data pipeline arrangement being employed.
Data Streaming refers to the continuous flow of data generated by various sources rather than in batches. This is useful for the data sources that can send the data in smaller chunks in a continuous flow as and when data is generated. Data streams can be generated by various types of sources in various formats and volumes. For example, data streams can be constructed from activity events triggered from web applications, telemetry from IoT or networking devices, server log files, banking transactions etc. Deploying well managed streaming data pipelines they can all be aggregated, filtered and sampled to seamlessly gather real-time information; thus injecting the data for the process of real-time analytics to extract business or operational insights.
Scalability: A data streaming application must be designed for scalability which can handle unexpected surge in data influx through the stream, being processed. Designing applications to scale is crucial in working with streaming data.
Ordering: The order of processing the data record in a chunk may be important in most cases from the operational and functional point of view. A good data streaming application must ensure that the ordering of data is maintained when processing it through the stream. A time series data stream won’t be useful if the datum in the stream is processed out of order.
Durability: Data consistency is always a hard problem to solve in data stream processing. The data read at any given time could already be modified and stale.
Fault Tolerance: Having a single point of failure at any processing unit in a data stream can pose serious disruption to the end-to-end data pipeline transmitting the data in stream. A data streaming solution must be designed to handle high availability and durability.
Security: Modern production grade data streaming applications need to make sure that the data is secure both in transit and when at rest.
Zaloni’s DataOps platform, Arena, uses data pipelines to improve efficiency and reduce costs while delivering trusted data to your consumers. Arena provides a DataOps approach to data management, with end-to-end visibility and control from source to consumer. Arena applies governance to each step in the pipeline to ensure data security and reduce risk.