With their ability to harness and make sense of all types of data from disparate sources, data pipelines are the foundation of modern analytics. A data pipeline refers to a series of steps (typically called jobs) that aggregate data from various sources and formats and validates this data in readiness for analytics and insights. Businesses can choose to build a data pipeline by hand-coding it, or can make a data pipeline with the help of a low-code/no-code tool to increase productivity and efficiency.
This article talks about the value data pipelines bring to organizations and takes you through the steps to build a data pipeline.
How Data Pipelines Help Answer Business Questions
Data insights help improve customer experience, save money, and help develop new products. For example, by leveraging Big Data Analytics, Netflix saved over $1 billion per year on customer retention. In addition, Uber employs real-time data analytics to run computations and calculate pricing and arrival time, improving the customer experience.
Naturally, data pipelines involve a series of jobs that may include:
- Cleaning data and removing null values.
- Creation of a new database from various data sources.
- Changing columns formatting
Data pipelines give businesses the flexibility to format and validate data for various use cases such as:
- Identifying the time most users are online to push advertisements.
- Identifying trends to help develop and test new products.
- Enhance customer experience through recommendations and predictive maintenance.
Building a Data Pipeline Tool Box
When choosing to build a data pipeline, there are three main architecture types organizations can choose from:
- Batch Architecture
- Streaming Architecture
- Streaming/Batch Architecture
Determining Your Data Pipeline Architecture
In choosing the right architecture approach when building a pipeline, data engineers need to ask the following questions:
- Data type, origin, and timeliness: What are the data sources, and in what formats do they arrive? If data arrives from various sources, a data integration pattern that ensures and maximizes the free flow of data is needed. If data is obtained directly from production databases, the organization must answer and find a solution in case ingestion affects the user experience. Secondly, regarding data type, data could be event-based, like real-time or in batches. With real-time data, data consumption and processing occur incrementally, which requires a highly scalable system to handle the high velocity of data over time. Batch-based data is normalized and can employ an incremental data ingestion system design, which helps reduce unnecessary load.
- Storage mechanisms: How large is the volume of data to be queried? Is the querying process an exploratory or pre-defined one? Defining the volume of data and the querying process helps decide the best storage options for your data. For example, Redshift is excellent at handling large volumes of data and exploratory querying. On the other hand, Hadoop shines for pre-defined queries and a limited amount of data. Additionally, a question of whether dashboard or analysis occurs within storage or if the data is moved elsewhere for analysis. Businesses also have to determine who will access the storage mechanism as some storage tools may require proficient technical knowledge.
- Budget: Open-source tools are great for building data pipelines as it helps reduce costs for pipeline development, but data engineers must be prepared to build and add additional configurations to best serve their business test cases.
Automating Your Data Pipeline Process
With so many considerations like dependencies, connections, and APIs when designing data pipeline architecture, pipeline data management can become challenging for data engineers.
Here are five automation principles to maintain when designing pipelines:
- Scalability: Business data tends to grow over time. Adding configurations to pipelines each time data exceeds capacity can quickly become time-consuming. An effective data pipeline should handle increasing data flow with time.
- Schedule: Data pipelines should also be automated to run on a schedule or based on an event rather than manually triggered to ensure scalability.
- Reproducible: Data pipelines should have a foundational layer that can help build several other pipelines to serve multiple use cases. This principle ensures quick reproduction and saves time and resources.
- Reliable and fault-tolerant: Data pipeline design should ensure data is readily available when needed. In the case of a failed job along the workflow, other downstream processes don’t proceed.
- Replayable: No matter how often a pipeline job runs, it should produce the same result given the same input. This principle helps ensure data consistency and quality.
Data Pipeline Construction Tools Consideration
Depending on the data pipeline architecture organizations choose to follow, numerous technologies are available to help build data pipelines from scratch.
- Data Ingestion: Data ingestion tools help collect data from various sources. The choice of data ingestion tools depends on whether the data streams are in real-time or in batches. For real-time data ingestion, tools like Apache Kafka and Amazon Kinesis, among the popular tool choices, offer low latency and are highly scalable.
- ETL tools: ETL tools are involved in data transformation and processing and involve deduplication, validation, removal of irrelevant inputs, and data formatting. Processing may occur in real-time or in batches. Popular ETL tools include Amazon Kinesis, IBM DataStage, and Talend OpenStudio.
- Landing Zones like Data Warehouses and Data Lakes: Data warehouses are data stores for processed data for business intelligence and analytics. They typically run pre-defined queries. The choice of data warehouses depends mainly on the data volume and use case. Big data warehouses like Google BigQuery, Snowflake, and Azure Synapse can handle large volumes of data. Data lakes like AWS Data Lake and Azure Data Lake offer storage solutions for exploratory data queries and storing raw data.
- Workflow: Most popular workflow tools like Apache Airflow and Luigi help ensure seamless flow in a data pipeline architecture. These tools are built with Python and help arrange and monitor jobs in a data pipeline.
The Easy Way to Build a Data Pipeline
StreamSets dataops pipeline enables organizations to build effective, reusable, and reliable data pipelines for their various business use cases. With over 16 out-of-the-box reusable connectors that facilitate your business data connection to multiple sources, StreamSets relieves organizations of the burden of setting up various integrations with time.
Additionally, StreamSets offers a multi-cloud platform for businesses to build. Here are some ways to create intelligent data pipelines with the StreamSets DataOps platform:
- How To Build Your First Data Pipeline with StreamSets Transformer Engine
- How To Build Your First Spark ETL Pipeline with StreamSets’ Transformer Engine
- Getting Started with Snowflake
- Build a Pipeline
- Build a CDC Pipeline
Data pipelines play a critical role in business analytics and decision-making, while providing the flexibility to handle peak demand and eliminate access barriers to insights and information.
Building data pipelines from scratch can be challenging as organizations must consider various factors like use cases for data, storage, frequency of ingestion, and data types. Additionally, effective pipelines must be secure, reliable, replayable, and capable of being automated.
Thankfully, businesses can leverage solutions that help build scalable, reusable pipelines for various business use cases.