A fundamental question among business data consumers is, “What is a data pipeline to begin with?”
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Data Pipeline Defined
Generally speaking, enterprise data pipeline solutions include a broad array of data-related tools, actions, and procedures, aggregated from several separate sources. The pipeline extracts, prepares, automates, and transforms data – without the need for tedious manual tasks related to extracting, validating, combining, filtering, loading, and transporting the data. This streamlined approach creates a simple flow between the data source and the target destination, saving a lot of time and effort for data teams. It minimizes errors and bottlenecks, improves efficiency at scale, and meets today’s demanding data management requirements.
Clean Data Wanted
Enterprises need a constant supply of fresh, reliable, high-quality data (a) to put together a complete 360 customer view and (b) to provide up-to-date operational intelligence that can be used to influence business outcomes. Each of these elements are difficult to attain. Together, they form one of the most significant challenges for data-centric organizations.
Current data processes and results let us know that there’s still a long way to go before reaching our data-driven vision. We witness some of the world’s most advanced companies invest countless resources in building and maintaining data lakes and warehouses, yet unable to turn their data into actionable business insights. Research shows that 85% of big data projects still fail, and 87% of data science tasks never even make it to production.
These disturbing results are what many businesses get despite having spent so much time and money on these data endeavors. Data scientists spend around 80% of their precious time on data pipeline tasks such as tracking, mapping, cleansing, and organizing. That means that only 20% of their busy schedules can be devoted to analysis and other meaningful assignments to move the organization forward. This “data disaster” leaves entire industries behind.
When you ask data scientists, engineers, and analysts the question, “What is a data pipeline?”, their answer is a single, end-to-end solution that can cover all the bases, or one technology hub to track, access, combine, cleanse, and prepare the required data and form managed datasets ready for use. Data teams can no longer rely on partial solutions, that only support some of the process, and fail to deliver governed data that can be seamlessly incorporated into the work process. Partial solutions that can’t handle massive volumes of data, and can’t be automated, only lead to more setbacks and confusion.
Data Pipeline Challenges
Enterprises aren’t asking what is a data pipeline – they want to know what it is in a data pipeline that works for them. Finding the right technology isn’t easy, as some formidable challenges need to be overcome:
Scale and speed
More data is generated today than ever before, and the massive amounts of data that companies and individuals create grow exponentially year after year. In 2020, we generated more data than expected and set a new record due to the digitization of the Covid-19 pandemic. Enterprises need pipeline solutions that can handle data at this volume and deliver the data quickly and continuously to any target system.
Data pipelining solutions must be able to accommodate more data, and more data stores.
Around 90% of today’s generated data is unstructured, with 95% of companies stating that managing fragmented data is their biggest business problem. An organization’s data capabilities are only as strong as its ability to aggregate, manage, and direct data on time. Considering the multiple resources and scale challenges we’ve mentioned, this obstacle is hard to overcome.
Automation and productization
Enterprises may want to develop their own data pipeline solutions, or choose platforms that can be independently customized to fit their needs, which, in theory, isn’t a bad idea. The main problem is that these technologies often lack sufficient automation capabilities, sending teams back to square one and the time-consuming, exhausting checklist of manual duties.
Finding the Right Data Pipeline Solution
The ultimate data pipeline platform should be capable of delivering fresh, trusted data, from all sources, to all targets, at enterprise scale. It must also be able to integrate, transform, cleanse, enrich, mask, and deliver data, however and whenever needed. The end result is not only clean, complete, and up-to-date data, but also automated data orchestration flows that can be used by data consumers.
Data pipelining, in a Data Product Platform, is fully automated, from ingestion to consumption.
Unlike traditional ETL and ELT tools, that rely on complex and compute-heavy transformations to deliver clean data into lakes and DWHs, data pipelining, in a Data Product Platform, moves business entity data via Micro-Databases™, at massive scale, ensuring data integrity and high-speed delivery.
The platform lets you auto-discover and quickly modify the schema of a business entity (such as a customer, product, place, or means of payment). Data engineers then use a no-code platform to integrate, cleanse, enrich, mask, transform, and deliver data by integrating entities – thus enabling quick data lake queries, without complicated joins between tables. And since data is continually collected and processed by Micro-Database, it can also be delivered to operational systems in real time, to support 360 customer views, operational intelligence, and other use cases.