Data Pipeline Architecture: A Complete Guide of Building a Data Pipeline
Data pipelines carry raw data from different data sources and database to data warehouses for data analysis and business intelligence (BI). Developers can build data Pipelines by writing code and interfacing with Saas Platforms manually. Nowadays, data analysts prefer using DPaaS (Data Pipeline as-a-service), which does not require coding.
To understand why DPaaS is preferred over conventional data pipelines, let us analyze the essential elements of data pipeline architecture and data replication.
Data pipeline architecture
A data pipeline architecture is the structure and layout of code that copy, cleanse or transform data. Data pipelines carry source data to destination. The following aspects determine the speed with which data moves through a data pipeline:
- Latency relates more to response time than to rate or throughput. Low latency can cost you more for the maintenance.
- Volume, or throughput, is the rate of data a pipeline can process within a specified period.
- A reliable data pipeline with built-in auditing, validation, and logging mechanisms improves data quality. Data Pipeline reliability requires all the connected systems to be free of faults.
Data engineers need to optimize these factors of the pipeline to accommodate the company’s needs. An organization must analyze business objectives, cost, and the type of computational resources when designing its data pipeline.
Designing a Data Pipeline
A data pipeline architecture is layered. The individual system feeds the data into the next one until it reaches its destination.
Data sources can be Data Lakes or Data Warehouses, where organizations first assemble raw data. Every company supports various data sources on their systems, and SaaS vendors also host multiple other data sources. Data sources are crucial for designing a data pipeline as they will make the first layer with quality data.
The ingestion of a data pipeline is a set of processes that will help to read data from data sources. An extraction process involves reading from the data source using API provided by it. Before data extraction, you have to perform data profiling to determine what data you want to extract based on its characteristics and structure and business requirements. Data ingestion might happen in batches or through streaming.
Batch ingestion and streaming ingestion
Batch processing is the process of extracting a dataset and administering the lot as a whole. The process works on a schedule based on external triggers. Batch ingestion is sequential. It does not involve new data but the dataset whose criteria is set by developers and analysts.
Streaming is an alternative to the data ingestion model where sources transfer unit data one by one. Companies mostly use batch ingestion for data transfer. Streaming ingestion is used only when they want real-time data for applications or analytics, having minimum latency.
The requirement determines whether the data will be moved into a staging area or sent directly along with its flow.
After data extraction, the format or structure might need adjustments. Data transformation is like the filtration of the data pipeline. The transformation includes filtering and aggregation. Data consolidation includes database combinations, where the relational data models can be used to combine related tables and columns together.
The timing of the data transformation depends on the data replication process running in the data pipeline: ELT or ETL. ETL (extract, transform, load) is an older technology which can transform data before reaching the destination. This technique is mostly used with on-premises data warehouses. ELT (extract, load, transform) carries data to the destination without affecting it. Data consumers can then implement the necessary transformations on the data within a data warehouse. ELT is quite popular among modern cloud-based data warehouses.
Destinations are data warehouses for data replication through the pipeline. Data warehouses are specialized databases that contain filtered data in a centralized location for data analysis, reporting, and business intelligence. Data lakes are places for less-structured data which can be accessed by data analysts and data scientists for relevant information.
An enterprise can also inject data into an analytics tool or platform that allows direct data feeds.
To keep the data pipeline operational, developers must code for monitoring and alerting data engineers regarding performance and resolve issues. While using data pipelines, businesses can either build their own or use a DPaaS.
Developers write, test, and maintain the code required for a data pipeline using different frameworks and toolkits.:
- Workflow management tools like Airflow and Luigi arrange the processes of building data pipelines. These open-source tools help developers to organize data workflows.
- Event frameworks like Apache Kafka and RabbitMQ support businesses to produce better data from their existing applications. These frameworks capture events from various applications and enable faster communication between different systems.
- Timely scheduling of processes is challenging in a data pipeline. There are tools available in the market that allow users to create detailed schedules directing data ingestion, transformation, and loading to destinations.
What is the problem in building a data pipeline architecture? In a company, developers and data engineers, rather than taking up the hassle of building and maintaining complex systems, are mostly busy on tasks that provide primary business value.
Nowadays, the DPaaS platforms help companies by removing the trouble of writing their own ETL code and building data pipelines from scratch. Daton is a simple data pipeline that can populate popular data warehouses like Snowflake, Bigquery, Amazon Redshift for fast and easy analytics using 100+ data sources. The best part is that you can use Daton is easy to set up without the need for any coding experience and it is the cheapest data pipeline available in the market.