The Data Pipeline forms the basis for data-driven work and thus increasingly represent the center of Data Driven Companies working with partners.
This efficient flow of data from one system to another, such as a SaaS application to a data warehouse (DWH), is what makes meaningful data analysis possible in the first place.
For this to happen smoothly, data pipelines are of great importance.
This article was written in German, automatically translated into other languages and editorially reviewed. We welcome feedback at the end of the article.
Data Pipeline: Definition
The data pipeline is a process that allows raw data to be collected from various sources and then stored in a data repository, such as a data lake or data warehouse, for further analytics operations.
Before information is fed into a data archive, the data is usually prepared.
This involves data transformations, such as filtering, enriching, and summarizing data to ensure appropriate data merging and normalization.
The following steps are automated:
- Loading data for additional analysis and visualization
This is especially important when the target for the data set is a relational database. This type of data repository has a defined schema that requires reconciliation - that is, matching data columns and types - to update existing data with new data.
Data pipelines in a business context
Data pipelines are an essential part of data science projects and business intelligence dashboards.
They serve as a "pipeline system" to collect and prepare data from various sources such as APIs, SQL and NoSQL databases, and files.
However, this data cannot be used directly and requires structured preparation by data scientists or data engineers to meet business use case requirements.
The type of data processing that a data pipeline requires is determined by a mix of exploratory data analysis and defined business requirements.
Once the data has been appropriately filtered, merged and summarized, it can be stored and made available for use.
Well-organized data pipelines form the basis for a variety of data projects, such as:
- Exploratory data analyses
- Data visualizations
- Machine learning tasks
Since Data Pipeline works with many data streams simultaneously, it can be used very efficiently.
This is how the Data Pipeline works
Three key steps represent the architecture of the Data Pipeline:
- Data collection:
There are different sources from which data can be collected that have different structures.
When using streaming data, the raw sources are often known as producers, providers, or senders.
Although companies can make the decision to extract data only when it is ready for processing, it is recommended to store the raw data in a data warehouse in the cloud first. This makes it possible to update historical data when data processing jobs need to be adjusted.
- Data transformation:
In this step, various tasks are performed to convert the data into the required format of the target data repository.
Automation and governance are used to facilitate repetitive workstreams such as business reporting and ensure that data is continuously cleansed and transformed.
For example, a data stream may be in a nested JSON format, which is resolved during data transformation to extract the relevant fields for analysis.
- Data storage
After the transformation, the data is saved in a data archive to make it accessible to different stakeholders.
For streaming data, this converted information is usually known as the user, subscriber, or recipient. Access to this data is therefore easy and fast for all parties involved.
Advantages of the data pipeline in the system
A plus point of data pipelines is that they view data as data streams, allowing for flexible schemas.
It does not matter whether the data comes from a static source (such as a flat file database) or a real-time source (such as transactions from an e-business solution).
The Data Pipeline is designed to process all these sources simultaneously and transmit them to a downstream system.
The target of this transfer does not necessarily have to be a data warehouse, but can also be another system, such as SAP or Salesforce.
Data Pipeline and ETL Pipeline: The difference
Often the terms Data Pipeline and ETL Pipeline (Extract-Transform-Load) are used synonymously - but this is wrong.
ETL pipelines represent a subcategory of data pipelines. 3 characteristics show this particularly clearly:
- ETL pipelines follow a specific sequence. Here, the data is extracted, transformed and stored in a data repository. However, there are also other ways to design data pipelines. In particular, with the introduction of cloudnative tools, the circumstances have changed. In these cases, data is ingested first and then loaded into the cloud data warehouse. Only then are transformations performed.
- ETL processes tend to involve batch processingbut as already mentioned, the scope of application of data lines is more extensive. They can also integrate the processing of data streams.
- Ultimately, although rather rare, it is not mandatory that data pipelines as a whole system perform data transformations as in ETL pipelines. Nevertheless, there is hardly any data pipeline that does not employ data transformations to facilitate the data analysis process.
Extract-Load-Transform for the Data Lake
In recent years, the ELT process as an alternative variant to the ETL process established.
In the ETL process, the data is first prepared, but this can lead to some information being lost. Originally, this process comes from the data warehousing area, where structured information is of great importance.
This contrasts with the ELT process, where data is first transferred to another infrastructure before being processed. This preserves as much of the original form and content as possible, which is especially important in the field of data science to train accurate machine learning models.
The ELT process is used primarily in the area of Big Data and Data Lakes, as unstructured data can also be processed effectively in this way. ETL and ELT are also generally referred to as "Data Ingestion", which includes data ingestion.
Types of Data Pipelines
There are two main types of data pipelines: batch processing and streaming data.
Batch Processing: Efficient but slow
Batch processing is a process of loading large amounts of data into a repository at predefined time intervals during off-peak hours.
This does not affect workloads on other systems, as batch processing usually involves large volumes of data that can load the entire system.
Batch processing is the optimal data pipeline when there is no direct need to analyze a specific dataset, but rather is associated with the ETL data integration process. This stands for "extract, transform and load".
Batch processing operations consist of a sequence of commands where the output of one command becomes the input of the next command. For example, one command may start a data ingest, the next command may trigger the filtering of certain columns, and the subsequent command may handle an aggregation.
This series of commands continues until the data is completely transformed and written to the data repository.
Streaming data / stream processing: Up to date but complex
In contrast to batch processing, so-called streaming data is used for data that needs to be updated continuously.
For example, applications or point-of-sale systems need real-time information to refresh inventory levels and sales histories of their items. This allows retailers to notify consumers whether a product is available or not.
A single action, such as a sale, is referred to as an "event," while related operations, such as adding an item to checkout, are typically categorized as a "topic" or "data stream." These events are then transmitted via communication systems or message brokers, such as the open-source Apache Kafka software.
Because data events are processed immediately after they occur, streaming processing systems have lower latency compared to batch systems.
However, they are considered less reliable because messages can be unintentionally discarded or remain on hold for a long time.
To overcome this problem, message brokers rely on confirmation procedures where a user confirms to the broker that the message has been successfully processed to remove it from the queue.
Use cases and tools of a data pipeline
Data management is becoming increasingly relevant due to the rise of Big Data. Data pipelines fulfill various functions, which is reflected, for example, in the following 3 use cases in the enterprise context:
Machine learning focuses on using data and algorithms to mimic the learning process of humans, thereby continuously increasing precision.
Statistical techniques are used to train algorithms to make classifications or predictions and to gain essential insights in data mining projects, such as in the Document Management with AI from Konfuzio.
Article examples on this area:
- IDP: Intelligent Document Processing Definition & Applications
- Text Mining Wiki - Definitions and examples of use
- Process Mining: The most important definitions and tools
Exploratory data analysis
Data scientists use the exploratory data analysis (EDA), to examine data sets and capture their key characteristics.
Data visualization methods are often used in this process.
EDA helps to process the data sources optimally in order to find the required answers and to uncover patterns and anomalies. In addition, hypotheses can be tested and assumptions verified.
Data visualizations present information using common graphical elements such as charts, plots, infographics, and even animations.
These visual representations of data make it possible to convey complex relationships and insightful data in a way that is easy to understand.
Data Pipeline: Examples
Data Pipeline has a wide range of uses, for example:
- Document Processing API: One possible application of Document AI in a German company is the implementation of a document processing API that enables documents to be automatically extracted and processed from various sources such as emails, PDFs or scans. Using machine learning models, the API can recognize important information such as names, addresses or order numbers and output them in a structured format. By implementing a document processing API, companies can streamline their data exchange process and reduce manual document processing. Especially when dealing with large amounts of data or complex documents, the API can add significant value and help improve the efficiency and accuracy of data processing. In addition, the extracted data can be directly integrated into other systems or processes to ensure seamless data exchange within the company.
- File Reader into DWH: A common use case is to easily read and reformat a file and then integrate it into a data warehouse. For example, one can import an Excel file using Python, perform transformation processes, and then store it in an Oracle database using SQL.
- Product Information API: Another orientation is offered by the Product Information API, which makes it possible to combine information from PIM and ERP by means of an ETL tool and make it available via an API. Whether as a file or REST API - the merging of data sources and their delivery to different channels often offers significant added value for the company.
- IoT Event Streaming: Another example of a complex pipeline is the transfer of data from an Internet of Things edge device to the cloud. Using event streaming, the data is transmitted in real time and stored in an unstructured database. Additionally, on-stream analytics are performed to ensure data quality. Due to the large volumes of data and the high demands on data processing, a high level of expertise and monitoring is required here.
Conclusion: Data pipelines are versatile and efficient
Use Data Pipelines to make your business more flexible and at the same time more efficient.
Batch streaming and stream processing capabilities make it possible to choose the right data processing method depending on the data.
Due to the wide range of applications, you can use Data Pipelines in different places and thus benefit from the advantages across the board.