It’s been said that more data has been generated in the last five years than in the entire history of humankind. Enterprises today grapple not only with the massive amounts of big data sources constantly churning out raw data, but even more so with making that data useful in real-time.
Figuring out how to make sense of all those datasets is key. Raw data contains too many data points that may not be relevant. So, data engineers have created data pipeline architecture — a structured system that captures, organizes, and routes data to drive business intelligence, reporting, analytics, data science, machine learning, and automation.
What is data pipeline architecture?
Data pipeline architecture organizes data pipelines to make data ingestion, reporting, analysis, and business intelligence easier, faster, and more accurate. It uses automation to manage, visualize, transform, and move data from multiple data sources in order to meet business goals. Data scientists and data engineering teams can then use the data for the benefit of the enterprise.
What are data pipelines?
Data pipelines are comprised of a sequence of data processing steps, facilitated by machine learning, specialized software, and automation. The pipeline determines how, what, and where data is collected and automates the process of extract, transform, load (ETL), validates and combines data, then loads it for analysis and visualization. The pipeline reduces errors, eliminates bottlenecks and latency — enabling data to move much faster and be made useful sooner to the enterprise than through a manual process.
Ultimately, data pipelines enable real-time business intelligence that gives the enterprise key insights to make nimble, strategic decisions that improve business outcomes.
Data scientists can then use the data to improve targeted functionality (use data pipelines to obtain insights into areas such as customer behavior, robotic process automation, user experience, customer journeys, to name a few) and to inform the business of key business and customer intelligence.
Why do you need data pipelines?
Raw data comes from multiple sources and there are many challenges in moving data from one location to another and then making it useful. Issues with latency, data corruption, data source conflicts, and redundant information often make data unclean and unreliable. In order to make data useful, it needs to be clean, easy to move, and trustworthy.
Data pipelines remove the manual steps required to solve those issues and create a seamless automated data flow.
Enterprises that use vast amounts of data, depend on real-time data analysis, use cloud data storage, and have siloed data sources typically deploy data pipelines.
But having a bunch of data pipelines gets messy. Which is why data pipeline architecture brings structure and order to it. It also helps to improve security, as data pipelines restrict access to data sets, via permission-based access control.
It’s all about making data useful as fast as possible to help the enterprise move with the speed, accuracy, and intelligence needed in a modern digital world.
What does data pipeline architecture look like?
Data pipeline architecture can be broken into various components, such as:
- Data sources – this is where data comes from and includes sources such as application APIs, clouds, relational databases, NoSQL, and Apache Hadoop
- Joins – the criteria and logic for how data is combined as it travels together in the pipeline
- Extraction – this can be specific data found in larger fields, which makes the data more granular
- Standardization – data needs to be standardized so it speaks the same language, uses the same units and is presented in the same way
- Clean up – this is where errors in data are caught and corrected or corrupt files are removed in order to ensure data quality
- Loads – clean data is loaded into a data warehouse, Snowflake, relational database, Hadoop, or a data lake
- Automation – this process handles error detection, reports, and monitoring and can be done continuously or on a schedule
Do you need data pipeline tools?
If you have significant data volume, siloed data, need real-time insights, and want to optimize automation across your enterprise, then data pipeline tools will make creating data pipelines easier for your organization.
What kind of data pipeline tools are there?
Data pipelining tools come in various types, including:
Batch processing – ideal for moving large amounts of big data on a regular basis, but not in real-time
Open source – built by the open-source community, a common open-source pipelining tool is Apache Kafka
Cloud-native – these tools are ideal for cloud-based data, like Amazon Web Services (AWS), AWS Lambda for serverless compute, or Microsoft Azure
Real-time – ideal for streaming data sources, such as the internet of things (IoT), finance, and healthcare
What about data integration?
Data integration is needed to pull data sources from on-premises and cloud sources into the data pipeline. For example, pulling data from your CRM into tools such as integration platforms as a service (iPaaS) automates the data integration and pipeline architecture process.
Questions to ask before you build a data pipeline
There are different designs for data pipelines — which is where an iPaaS, such as SnapLogic, can help you quickly determine the easiest and most effective pipeline design.
Before you build a pipeline, here are some things to consider:
- What do you want the pipeline to accomplish? Will it move data repeatedly? What business process or workflow will it enable or support?
- What types of data will you be working with? Structured data, unstructured data, streaming data, or stored data? How much?
- Does the pipeline need to be built from scratch by data engineers or can a tool such as SnapLogic that comes with 500+ pre-configured integration Snaps, enable you to quickly build pipelines with no/low code ease?
- Who in the organization needs to be able to build and use data pipelines? Increasingly, business decision makers and non-DevOps employees are needing to be able to quickly and easily build pipelines without having to wait for a data science team member to do it for them. What use cases do you have? What use cases can you anticipate for the future?
Building data pipelines and data pipeline architecture will enable your enterprise to scale, move faster, and ensure that it harnesses the true power of data to achieve its outcomes.