Organizations often get data from varying sources. The data could be structured, semi-structured, or even unstructured data like audios or videos. Data is a key asset for any organization irrespective of its domain and size.
A well-defined, reliable, scalable and business-driven data ecosystem plays a vital role in determining business outcomes. It is very important for a data-driven organization to capture, process and analyze data to understand different business metrics.
Organizations often collect data from different sources. This data varies in volume, variety, and velocity. All of this data can drive many business decisions and can be leveraged by the business and analytics team in different ways to provide data-driven answers to business questions. A data warehouse stores this data in a cleaned and structured way, which can then be used by different stakeholders as per their need.
Now, you might think, if a data warehouse stores clean data, there must be some process that is responsible for cleaning this data. Right? Yes, that is what a data pipeline is.
That being said, let’s first understand data warehouses and data pipelines.
What is a Data Warehouse?
A data warehouse is a database system encompassing multiple tables interconnected using a Star or Snowflake schema design. A data warehouse is a common system built for business reporting and analysis. It is a clean, organized, business driven, single representation of the data. Data warehouses often store data from multiple source systems. These could be databases, file systems, CRM systems, etc. in a structured way to perform analysis, generate business reports, and extract meaningful information.
Organizations need this data to be stored in a single place so that it can be leveraged by multiple stakeholders, such as data scientists, business analysts, and project managers, for reporting and analysis purposes. When it comes to building reliable, low cost, scalable data warehouses, cloud data warehouses are the first choice. These warehouses work on pay-per-use costing models, are highly scalable and fully managed by cloud vendors.
What is a Data Pipeline?
A data pipeline is a series of processes/stages running in sequence or in parallel to accomplish any required outcome.There are a series of stages wherein each stage delivers an output that eventually becomes an input for the very next stage. This process continues until the pipeline is completed and the outcome is achieved.
A data pipeline consists of three major steps: a source (it could be files, databases, CRM systems etc.), a processing stage (it could be a tool, eg. Informatica, or a framework, eg. Spark, to process the data), and a destination (it could be databases, eg. AWS S3). Data pipelines enable the flow from operational databases to data lakes, from data lakes to analytics databases, from data lakes to data warehouses, and can be used to build other pipelines for providing data to different systems.
For example, let’s consider an example of a social media comment. This comment might trigger multiple data pipelines in the backend, such as a running sentimental analysis pipeline, which outputs positive, negative, or neutral comments, or a running data warehouse pipeline to ingest comments in data warehouse for real-time reporting. Though the data is from the same source in both cases, the underlying data pipelines are different.
Common steps in a data pipeline include cleaning, pre-processing, transformation, enrichment, filtering, aggregation, and running business algorithms against the data.
Data Pipelines with Data Warehouses
Organizations likely deal with massive amounts of data. To analyze that data, organizations need a single view of that data and for doing so, they build data warehouses, which are responsible for capturing history and providing a single view of the data. When this data resides in multiple source systems and applications, this needs to be combined and processed in a way that makes sense for in-depth analysis and reporting.
Data pipelines are responsible for processing and combining this data from multiple sources and loading it into data warehouses. There could be different design patterns for building data pipelines based on system and business requirements but without a data pipeline a data warehouse can not be built.
Data pipelines are of critical importance when building systems that highly rely on data points. As the role data plays in businesses increases, the demand to capture, process, and validate data at every single point increases. Thus, data pipelines often have stages for data validation to meet business expectations. They eliminate most manual steps while moving data between multiple stages, and provide smooth, validated, automated data flow. These are very essential for real-time analytics and making faster, data-driven decisions.
Data Pipelines vs ETL
Data pipelines and ETL pipelines are processes that go hand in hand. Although both are highly correlated, they are not actually identical. The role of data pipelines and ETL pipelines is to move data from one location to another, but the key difference lies in its design, implementation, and use case.
ETL pipelines use a series of stages which extract data, transform data, and load it into the target. This target could be a data warehouse, data mart, or even a database system. On the other hand, data pipeline is somewhat broader terminology and consists of ETL as a subset. It includes a set of processing tools to move data from one location to another, but data may or may not be transformed.
Data pipelines enable the flow of data from one location to another. Data warehouses are built keeping business requirements at their center. Since organizations have different source systems to capture day to day information (aka operational databases), we need to build a solution that can capture this data, process it, and load it into well-architected data warehouses.
Data pipelines are often built using ETL as a subset to complete the data warehouse flow and make historical and the latest data available to business stakeholders.
I hope this article helped you understand how data pipelines work with data warehouses and how they are different from ETL pipelines.