Just as vendors rely on U.S. mail or UPS to get their goods to customers, workers count on data pipelines to deliver the information they need to gain business insights and make decisions. This network of data channels, operating in the background, distributes processed data across computer systems, an essential framework and function for any data-driven business.

The value of connecting data systems with pipelines continues to grow as companies need to consume lots of streaming data faster, served out in various formats. So, managers who understand data pipelines at a high level can better move raw data toward the information seen on dashboards or reports, most economically. 

What Are Data Pipelines?

Data pipelines describe data processing elements connected in series, with the data output of one channel acting as the input for the next one. These conduits start at the source, where systems ingest it by shifting or replicating it and moving it to a new destination. 

Computer programs create, modify, transform, or package their inputs into a more refined data product at that new spot. Then, another computer system may take the processed data outputs, in its data pipeline, as inputs. 

The data continues along each connection and through different cleansing processes and pipelines until it reaches a consumable state. Then the employees use it on the job, or that data gets stored in a repository, like a data warehouse.

In addition to transporting data, some conduits clean, convert, and transform the data as it moves through them, similar to how a person’s digestive tract breaks down food. Other data channels collect and analyze data about the organization-wide pipeline network, providing end-to-end monitoring of its health, also known as data observability.

Why Do Companies Use Data Pipelines?

Companies find good data pipelines scalable, flexible, maintainable, and fast. Automated data pipelines, created and managed by algorithms, can appear, or retract when needed. Also, data pipelines can reroute data to other conduits avoiding a data jam and transporting data quickly.

Data pipelines contribute to different critical Data Management needs across the enterprise. Examples include:

• Data Integration: Connectors that package and transport data from one system to another and include event-based and batch processing of data streams
• Data Quality/Data Government: Conduits that define and enforce Data Quality rules per corporate policies and industry regulations for the data output
• Data Cataloging/Metadata Management: Pipelines that connect and scan metadata for all types of databases and give enterprise data context 
• Data Privacy: Channels that detect sensitive data and protect against breaches

Three Challenges Faced by Organizations

Organizations leveraging data pipelines face at least three challenges: complexity, increased costs, and security.

Complexity

Engineers must attach or change data pipelines as business data requirements change, increasing the complexity of using and maintaining the channels. Furthermore, employees need to move data across interlinking hybrid cloud environments, including on-premises ones publicly available, like Microsoft Azure. 

Handling many different cloud computing locations adds frustrations with data pipelines because of challenges in scaling the data pipeline network. When engineers fail to architect competently, the data channels across an organization, data’s movement slows, or employees fail to get the data they need and must do additional data cleansing.

Gur Steif, president of digital business automation at BMC Software, talks about how corporations struggle to embed an intricate pipeline system into their critical applications. Consequently, enterprises will need to invest in data workflow orchestration platforms that keep the data flowing and require sophisticated DataOps knowledge.

Increased Costs

As newer data technologies emerge, businesses face increased costs to modernize each of their data pipelines to adapt. In addition, companies must spend more on pipeline maintenance and advancing technical knowledge.

Another source of costs originates from changes made by engineers upstream, closer to the source. Sometimes, these developers cannot directly see the ramifications of their code, breaking at least one data process as the data travels down the pipelines.

Data Security

Engineers need to ensure data security for compliance as data flows down different data channels to audiences. For example, company accountants may need sensitive credit card information sent through the pipelines that should not go to customer service staff. 

So, the security risks grow if engineers do not have a way to view the data as it flows down the pipeline. Ponemon Research notes that 63% of security analysts call out the lack of visibility into the network and infrastructure as a stressor.

Best Practices for Using Data Pipelines

Using data pipelines requires striking a delicate balance in making necessary data accessible to users as quickly as possible at the lowest cost for creation and upkeep. Certainly, enterprises need to choose the best Data Architecture with secure, agile, and operationally robust data pipelines.

Additionally, companies need to consider the following:

• AI and machine learning (ML) technologies: Organizations will rely on ML to identify data flow patterns, best-optimizing data flow to all parts of the organization. Additionally, good ML services will make data flow more efficient by facilitating self-integrating, healing, and tuning data pipelines. By 2025, AI models will replace up to 60% of existing ones, including those with data pipelines built on traditional data.
• Data observability: Data observability provides engineers with a holistic oversight of the entire data pipeline network, including its orchestration. With help from data observability, engineers know how the data pipelines are functioning and what to change, fix or prune.
• Metadata management: Getting good data observability requires making the best use of metadata, also known as data that describes data. Consequently, companies will apply a metadata management structure to combine existing with emerging active metadata to get the desired automation, insight, and engagement across data pipelines.

Tools That Help Manage Data Pipelines

Businesses depend on data pipeline tools to help build, deploy, and maintain data connections. These resources move data from multiple sources to destinations more efficiently, supporting end-to-end processes.

While some enterprises plan on developing and maintaining specialized internal tools, they can drain the organizations’ resources to manage them, especially when data circulates in multi-cloud environments. As a result, some businesses will turn to third-party vendors to save these costs.

Third-party data pipeline tools come in two flavors. Some generic ones collect, process, and deliver data across several cloud services. Examples include:

• AWS Glue: A serverless low code, extract, transform, load (ETL) platform that has a central metadata repository and uses ML to deduplicate and clean data
• Azure Data Factory: A service for orchestrating data movement and transforming data between Azure resources, using data observability, metadata, and machine learning
• Cloudera: Data services that handle data across several enterprise clouds, streamline data replication, and use NiFi – a fast, easy, and secure data integration tool
• Google Cloud Data Fusion: A high-end product and foundation of Google Data Integration that includes data observability and integration metadata.
• IBM Information Server for IBM Cloud Pak for Data: A server with data integration, quality, and governance capabilities, using ML capabilities
• IBM Infosphere Information Server: A managed service on any cloud or self-managed for a customer infrastructure that uses ML
• Informatica: An intelligent data platform that includes native connectivity, ingestion, quality, governance, cataloging through enterprise-wide metadata, privacy, and master data management across multiple clouds
• Talend: An entire data ecosystem that is cloud-independent and embeds ML throughout its data fabric

Other tools specialize in preparing and packaging data for delivery:

• Fivetran: A low-setup, no-configuration, and no-maintenance data pipeline that lifts data from operational sources and delivers it to a modern cloud warehouse 
• Matillion: A dynamic ETL platform that makes real-time adjustments if data processes take too long or fail
• Alooma: A data pipeline tool from Google for easier control and visibility of automated data processes
• Stitch: An ETL and data warehouse tool, paired with Talend, that moves and manages data from multiple sources

At the enterprise level, businesses will use at least one generic data pipeline resource that spans services across multiple clouds and another specialized one to handle the intricacies of data preparation. 

Conclusion

Any modern Data Architecture requires a data pipeline network to move data from its raw state to a usable one. Data pipelines provide the flexibility and speed to best transport data to meet business and Data Management needs.

While poorly executed data pipelines lead to increased complexity, costs, and security risks, implementing a good Data Architecture with good data tools maximizes the data pipelines’ potential across the organization.

As Chris Gladwin, co-founder and CEO at Ocient, notes, data pipelines will become more essential to ingest a wide variety of data well. The future brings data pipeline improvements with more sophisticated data integration that is easier to manage.

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