Organizing data for robust business intelligence, tactical insights, and analytics always starts with data pipelines. However, most businesses deal with enormous quantities of data originating from diverse sources, housed in various cloud infrastructures, and available in a wide range of formats; as a result, silos are an inevitable outcome.
Establishing a comprehensive and unified understanding of one’s data is critical for making informed decisions, improving productivity, and discovering profound insights. That’s why knowing what a data pipeline is and how to operationalize it is crucial.
What is a Data Pipeline?
A data pipeline consists of an ensemble of tasks and tools enabling data transfer from one system, maintaining its storage and processing techniques, to another system where it can be administered and preserved – focusing on specific business requirements.
Further, pipelines facilitate the automated retrieval of data from numerous sources, followed by its conversion and consolidation into a single, high-performance data storage system. This is critical to modern enterprises with sizable IT and digital dependencies.
Think of yourself as an analyst of different data types, demonstrating how people interact with your brand. This might include the user’s location, gadgets, session recordings, history of transactions, customer service interactions, and any feedback they provided. Subsequently, this data is gathered in a warehouse linked to a CRM, generating a unique profile for every customer.
All and any data user who needs it to build and maintain analytical tools or to make strategic and operational decisions can do so with ease and agility, owing to the aggregation enabled by data pipelines. These individuals are marketers, data science groups, BI experts, chief product officers, or any other professional who relies heavily on data.
For CIOs today, ensuring proper architecture and operations of enterprise data pipelines is a central part of their responsibility.
Why Do You Need Data Pipelines? Key Benefits
Some level of data ingress and outgress will occur from your systems, and without data pipelines, these will form an unstructured, inefficient process. Conversely, by investing in their data pipelines, CIOs and IT managers can:
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Improve data quality
Dataflows are vulnerable to obstacles and corruption at numerous points. However, data pipelines help in the continuous organization of data. They facilitate and make monitoring available to all users. Additionally, they integrate data from various sources and systems to improve the information’s reliability, accuracy, and usability.
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Automate data operations
Decomposing a data pipeline into repeatable stages facilitates automation. Minimizing the probability of human error allows seamless data transmission and expedites processing. Also, concurrently handling multiple data streams can be achieved by eliminating and automating redundant stages – driving efficiency.
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Power more accurate analytics
Data extracted from diverse sources holds unique characteristics and comes in various formats. A data pipeline supports the editing and transformation of diverse data sets, irrespective of their unique attributes. The focus is on consolidation to optimize analytics, allowing for a more seamless integration with business intelligence apps.
Building a Data Pipeline
When building data pipelines, technology leaders typically choose one of two options – batch processing and streaming data pipelines. Each is suitable for a different use case, as explained below:
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Batch processing pipelines
As the name implies, batch processing loads “batches” of data onto a repository at predetermined time intervals. Batch processing tasks frequently manage substantial quantities of data, thereby putting a strain on the entire system. Therefore, this process is scheduled during non-peak business hours to minimize the interruption of other assignments.
Generally, batch processing is considered the most suitable data pipeline method for tasks like monthly accounting, which don’t involve immediate analysis of a specific dataset.
The steps in this instance will consist of a series of sequential commands wherein the outcome of one command acts as the input for the next one.
An excellent example of this could be when a single command initiates the action of ingesting data; another could trigger the filtering of particular columns, and yet another might be responsible for aggregation. This command sequence continues until the data undergoes a comprehensive transformation and has been added to the repository. Hadoop and MongoDB are examples of this type of data pipeline at work.
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Streaming data pipelines
Unlike sequential processing, streaming data is used when continuous updates to the data are necessary. Apps and point-of-sale systems, for instance, demand real-time data to refresh product inventories and sales histories.
An “event” in the context of streaming data pipelines is a singular occurrence, like the sale of a software product. As an illustration, adding an item to the transaction is referred to as a “topic” or “stream.” In turn, these events pass through messaging infrastructures like Apache Kafka.
As a result of the immediate processing of data events that transpire, streaming systems show reduced latency compared to sequential systems.
They are less dependable than bulk processing pipelines, as messages can get deleted accidentally, or too many messages may clog the queue.
To tackle this problem, messaging systems add a functionality called “through acknowledgment.” In this phase, the data pipeline checks if a data message has been successfully processed, letting the messaging system eliminate it from the stack.
CIOs must consider the specific needs of their organization and each business unit when evaluating data pipelines. But regardless of which pipeline you choose for an application, it will consist of a few key components.
The Essential Components of Data Pipelines
A data pipeline will include:
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Origin:
Origin is the starting point of a data pipeline, where data is entered. Your business’s IT environment will have numerous data sources (transaction apps, connected devices, social networks, etc.) and storage facilities (data warehouses, data lakes, etc.) –these will all serve as the origin.
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Dataflow:
This is the transfer of data from its point of origin to its final destination, spanning both the adjustments it undergoes during transit and the data repositories it passes through. This component is often referred to as ingestion.
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Preparation:
Before implementation, it may be necessary to cleanse, aggregate, transform (including file format conversion), and compress data for normalization. Preparation is The process that alters data to make it suitable for analysis.
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Destination:
Data transmission ends at a location known as the “destination.” The destination is usage-dependent; for instance, data can be obtained to strengthen and expand data visualization or other tools for analysis. Or, it may fuel a security automation system like SIEM.
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Workflow:
The workflow establishes a series of actions and their interactions within a data pipeline. Upstream jobs are tasks executed on the data close to the resource from which the data reaches the pipeline. Downstream activities take place in closer proximity to the final product.
In Conclusion: Selecting Your Data Pipeline Toolkit
An organization looking to build and strengthen its data pipelines should consider implementing the following:
- Data lakes: Data lakes are often used by organizations to construct data pipelines for machine learning and AI initiatives. For massive data volumes, all major providers of cloud services — AWS, Microsoft Azure, Google Cloud, and IBM — offer data lakes.
- Data warehouses: These central repositories retain processed data strictly for a specific purpose. Teradata, Amazon Redshift, Azure Synapse, Google BigQuery, and Snowflake are popular warehousing alternatives.
- ETL (extract, transform, load) tools: ETL features a variety of tools for data integration and preparation, including Oracle Data Integrator, IBM DataStage, Talend Open Studio, and several others.
- Batch workflow schedulers: Programming tools like Luigi or Azkaban support the creation of sub-processes as a set of tasks with interdependencies. It’s also possible to monitor and automate these workflows.
- Data streaming tools: These tools can perpetually process data gathered from sources such as IoT and transaction systems. Google Data Flow, Amazon Kinesis, Azure Stream Analytics, and SQLstream are a few examples.
Uber uses streaming pipelines built on Apache to gather real-time data from chauffeur/driver and passenger applications. By leveraging data channels encompassing both on-premise systems and Google Cloud, Macy’s makes sure that every customer enjoys an equally compelling experience, whether they are in-store or purchasing online. No matter your industry, efficient data pipelines are crucial for modern, data-driven businesses.
You can supercharge your operations using data by zeroing in on the exemplary pipeline architecture and the most optimal toolkit.
