In today’s digital world, big data is a significant driver of business success. Businesses use this information to gain insights and make data-driven decisions.
Companies must collect, store and process data from many sources. Considering big data’s volume, variety, and complexity, completing these processes can be challenging. Fortunately, we can configure a data pipeline to automate data transformation and movement from the source to the target destination.
Let’s explore what a data pipeline is and how it works.
Understanding Data Pipelines
To better understand data pipelines, let’s imagine two possible scenarios for a coffee bean delivery service. In both scenarios, the process starts with sourcing green coffee beans from varied suppliers.
In the first scenario, the coffee beans are roasted and then transported to consumers via a delivery driver. When they arrive, they are ready for consumption. This scenario represents an ETL (extract, transform, and load) data pipeline. Just like the beans are roasted before they are delivered, in an ETL pipeline, data is transformed before it is loaded.
Alternatively, the business can deliver its green coffee beans directly to a cafe, unroasted. The cafe can then roast the beans before consumption. This represents an ELT (extract, load, and transform) data pipeline, where data is transformed after it is loaded.
Both coffee bean scenarios highlight the primary intention of a data pipeline — to move data effectively. Whether it’s an ETL or ELT pipeline, a data pipeline moves data to its intended destination and transforms it to its intended form.
A data pipeline service typically includes processes like ingesting raw data from various sources and moving it to a storage system for analysis and visualization. The whole procedure relies primarily on automation. A data pipeline’s goal is to ensure that it extracts, cleans, transforms, validates, and loads data for further analysis automatically.
Today, the majority of data pipelines depend on the ELT process. This is due to the high adoption of cloud data warehouses that scale according to users’ processing needs and provide unlimited storage.
However, we still need ETL data pipelines when working with relational databases. These databases are pre-structured and therefore require data to be transformed in order to load. We need to transform the data into a relational format so the warehouse can ingest it. This transformation process may include data mapping to combine multiple data sources.
Data Pipeline Elements and Processes
A data pipeline comprises multiple processes and tools that move data from one system to another for storage and handling. We’ll discuss the methods, techniques, and technologies associated with data pipelines in detail below.
Data pipelines accommodate streaming and batch data so that they can extract data from any source. Relational database management systems (RDMS), application APIs, social media management tools, and IoT device sensors are examples of such data sources. Other examples are storage systems, like data lakes and data warehouses.
The pipeline extracts source data through webhooks, push mechanisms, and API calls in real-time or scheduled intervals.
After the pipeline extracts data from various sources, it sends the data into production for manipulation. The next steps are defined by an organization’s specific needs, but typical steps in the data processing stage include aggregation, filtering, augmentation, grouping, and transformation.
Several changes can happen as data travels from one node to another. The data can become corrupted, and data sources may generate duplicates during data flow. So, the data undergoes correction and standardization.
Tools review the corrupted data separately or entirely remove it from the pipeline during the correction step. Standardization ensures the data meets industry standards on attributes (such as color), units of measure, and dates. Data must be in a consistent format for comparison. For example, all data must be measured in pounds.
Data tools periodically transform collected data in batches (batch processing) or the pipeline (stream processing). Sometimes this happens even before loading it in a new database. Data workflows can include dependencies that determine how data validation and collection are automated.
Data comes out of the processing stage clean — free of corrupted or excess information and in a consistent format — and ready for loading into a system for analysis. A data storage location, like a data lake, may not be the final destination. We may configure a pipeline to route data through various applications such as visualization, deep learning, or machine learning models.
The automation process in a data pipeline handles monitoring, status reporting, and error detection. Monitoring a data pipeline that includes real-time data and processes data in large volumes helps ensure all the processes are running correctly and performing their intended purpose.
A data observability architecture can monitor and alert latency, saturation, errors, and traffic. Tools like the Dataflow monitoring interface help diagnose and fix problems in pipelines.
As data velocity, variety, and volume expand, data pipelines become an integral part of many modern organizations. A data pipeline automates data aggregation, cleaning, transformation, and distribution.
CData Sync helps us build pipelines with high-performance ELT. Plus, you can use the CData platform to connect, automate, and integrate data. Request a demo to learn how CData fits into your data pipeline.
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