Successful businesses are the ones that create and harness the most data. Of course, that statement is subjective but it does ring true for many companies that aspire for growth. Businesses are constantly seeking ways to begin streaming data from multiple data sources into one platform, such as cloud analytic platforms like Snowflake and Databricks, to gain relevant insights for their business. These insights have the ability to create a significant competitive edge over rivals.
Getting the data from one location to another, efficiently and in real-time, is the true challenge of modern data integration. Creating real-time data pipelines is one approach that makes it easier to move data from multiple origins to a destination. Most of these can be simple to set up and allow users to quickly extract value from the data through analytics.
This article aims to elaborate on the concept of data pipelines in a simplified way so you can fully understand the concept. We will cover a few main topics, including the definition of a data pipeline, use cases for data pipelines, must-have elements of a modern data pipeline, and ultimately, the steps involved in the creation of a data pipeline. Let’s jump in!
What Is A Data Pipeline?
Although in the architecture of a modern system, we tend to think of a data pipeline as a single entity, it can actually be relatively complex. A data pipeline is a series of data processing steps that involve the movement of data from one place, known as the source, to another, known as the destination. Along the way, the data may be transformed and optimized through filtering, cleaning, aggregating, and enriching, before arriving in a state that can be used for analysis to garner business insights. Depending on the technology used, the actual process mentioned can vary.
Simply put, a data pipeline can be described as a sequence of actions that automate the collection, movement, and transformation of data between a source system and a target repository. In the data pipeline process, workflows are defined so that a task can be dependent on the successful completion of a previous job, that is, each step delivers an output that becomes the input for the next step until the data pipeline process is completed. The work required to get the data to its desired end state will usually determine the complexity of the pipeline. Some pipelines may require a massive amount of data transformation and some may require none at all.
Data pipelines can unify data from any number of sources, it’s not rare to see a pipeline have many data sources that use it to move data into the target platform. As the data pipeline gets raw data from disparate sources, it transforms the raw data into a specific format and moves it into the target platform. Since the data is refined and transformed within the pipeline, by the time it gets moved into the destination, it is ready for data analysis, data visualizations, machine learning, and business intelligence. These use cases are not possible with the data in its raw forms, either because it is not formatted correctly for this type of analysis or because multiple data points from different sources are required to gain the desired insight.
Data Pipeline Use Cases
Data pipelines can be used in different sectors and companies serving various functions. Although each use case may be highly customized when it comes to implementation, one thing is for sure: data pipelines are a crucial part of any system architecture. Below are a few examples where data pipelines are necessary and useful.
- Processing And Storing Transactions: Data can be processed and moved to a more appropriate system to enable reporting and data analytics. Using a platform that specializes in transaction analytics can provide useful insights for business products and services.
- Providing A Unified View: Many businesses benefit from having a location where organization-wide data can be accessed in a single place. A data pipeline can be used to consolidate data from diverse sources into a single data store. This can be beneficial in providing organizations with a unified source of truth.
- Data Visualizations: Data visualization is the representation of data through graphics like charts, plots, infographics, and animations. Visualizations are a great tool to communicate data relationships and give data-driven insights in a simplified and easy-to-understand way. Using a data pipeline to consolidate the necessary data for data visualizations is crucial for delivering holistic insights.
- Machine Learning: This branch of Artificial Intelligence (AI) and computer science involves the use of data and algorithms to enable computers to imitate the way humans learn and improve accuracy along the way. Algorithms are trained to make classifications and predictions, through the use of statistical methods. For Machine Learning, the more data the machine has, the more accurate the predictions. For this, data pipelines are essential to feed all of the source data into the algorithm.
- Improving Backend Systems: Backend system performance is improved by reducing the load on operational databases as data is being migrated to large data stores. Data pipelines can accurately and efficiently offload data to another platform without putting a load on the source systems. If the data is being transferred for read-only purposes, this can also help to offload queries that can bog down backend systems. We wrote more about this concept in this blog.
- Exploratory Data Analysis: Exploratory Data Analysis (EDA) is used to analyze and investigate data sets summarizing their characteristics. It helps in determining the best ways to manipulate data sources making it easier to check for anomalies, discover hidden truths, test hypotheses, etc.
- Ensuring Data Quality: Data quality, reliability, and consistency are greatly enhanced using a data pipeline. Since the pipeline can help with all of these critical factors while data is in transit to the destination, data becomes a lot more useful because of the improved quality. The quality of the data helps to determine the quality and accuracy of the insights gained from it.
Elements of A Data Pipeline
As mentioned before, even though architecturally a data pipeline may look like a single entity, it does contain multiple parts. Various components make up an effective data pipeline and must be considered when building a data pipeline. We are going to discuss these elements below so you can get a better understanding of the components needed to create an efficient data pipeline to meet the demands of your business.
This is the first element of a data pipeline and refers to where the data originates from. A data source can include any source that generates data for your business. These sources can include transactional data, relational databases, analytics data, SaaS applications, IoT devices, social media, and third-party data.
The next element is the mode of data collection and ingestion. Data needs to be moved into the pipeline, collection/ingestion refers to the process or mechanism that will be used to move data into the data pipeline. Ingestion tools are used to connect to various data sources and can collect and ingest data through a few different mechanisms. One possible way is to ingest data through a batch processing model where data is collected periodically. Another way to ingest data is through a stream processing model in which data is sourced, manipulated, and loaded as soon as it is created. Stream processing is the only way to achieve real-time data pipeline functionality in an efficient manner. Most pipelines ingest raw data from data sources through a push mechanism, an API call, a replication engine that pulls data at regular intervals, or a webhook that synchronizes data in real time or at fixed intervals.
This component is where, if applicable, the data is transformed into a suitable state. Not all data pipelines will do heavy data processing or transformation but some will extensively. Typically, any processing is done using ETL or ELT processes to get the data into the correct state and to make it possible for the data to be analyzed by the target platform. The data processing may involve data standardization, validation, sorting, normalization, verification, deduplication, and enrichment depending on your business-specific needs and the platforms that the data will be moved into. An ETL process is often used when the data is to be stored in a data warehouse. This is because data warehouses have a predefined schema that must be adhered to when data is replicated onto the platform. On the other hand, an ELT process is used for platforms such as data lakes since the data can be loaded into the destination first and then can be transformed into a useful state.
This is where the data is actually stored. The storage itself can be in a wide variety of data stores like data warehouses, data lakes, and data marts. The data warehouse, be it on-premise or cloud-based, stores data in a structured format. A data lake is slightly more flexible in terms of data storage and can accommodate structured, semi-structured, and unstructured data.
This layer of the data pipeline involves tools that can be used to deliver, integrate, and consume data from data stores to be used by businesses for analytics. The analytics can sometimes be extracted within the target platform or may be done through other 3rd-party analytics tools. The consumed data may be leveraged through SQL, batch analytics, reporting dashboards, or even machine learning.
This part of the data pipeline is crucial for safeguarding data and ensuring data security and governance. It is simply the monitoring of elements to ensure data integrity to avoid system failures, such as network congestion and latency. Methods that can be used for data governance include enforcing encryption, auditing mechanisms, usage monitoring, and network security. If there are any issues, the data governance component should monitor all the operations going on in the data pipeline and alert administrators when potential issues arise.
How To Design A Data Pipeline
Now that we have looked at all the components of a data pipeline, let’s take a dive into the factors to look at when deciding to build a data pipeline. We will examine some important points and give some steps to ponder to ensure good design decisions and the right information to build efficient data pipelines to meet your specific business requirements.
Determine The Goal
You have to determine the value you are trying to achieve by setting up a data pipeline. This step includes asking relevant questions to discover the core use case of the data pipeline, the business objectives for setting up the data pipeline and putting benchmarks in place to determine the business and technical success of the data pipeline. These factors will help you to narrow down the list of tools needed in order to build out your data pipeline.
Choose The Data Sources
Determining the potential sources of your data is the next step to consider in designing your data pipeline. By knowing where the data is coming from and if there will be multiple sources or just a single source, different solutions will become more or less relevant. After outlining the data sources, the data format and the connection mode to extract the data from the data sources will also need to be considered. This will further narrow down the list of suitable technologies to build the data pipeline with.
Determine The Data Ingestion Strategy
Knowing how the data would be ingested into the pipeline should be the next point of consideration. This would involve determining the communication layer to be used, for example, will the data be sent through HTTP, gRPC, or MQTT. You’ll also need to determine if the data ingestion will need to be done in batches or in real time. You may also need to consider if intermediate data sources are used to hold data before it is sent to the destination and if data would be accepted from a third party. Certain tools may only support specific ingestion strategies, further narrowing down the options which are suitable for your use case.
Design The Data Processing Plan
This would involve you deciding on the transformation process needed to get the data into the format which is most applicable. In some cases, there may be little to no data processing needed at all. In other cases, multiple technologies could be required, such as ETL, ELT, cleaning, or formatting, to get the data into an acceptable format for analytics or ML use cases. Deciding on a data enrichment strategy and determining the volume of data to be replicated through the data pipeline should also be thought of in the data processing plan. Processing may require a single tool or possibly even multiple tools that will work together to format the data.
Set Up Storage For The Output Of The Pipeline
Having gone through the previous steps above, the next step is to determine the final destination for the data. This should be done based on your business needs so the platform where the data will finally reside will accommodate your needs. The output could be a platform such as a data lake or a data warehouse, cloud or on-premise, and could be stored in a wide array of formats depending on the platform chosen. By knowing the source and the target, the input and output of the data pipeline, various tools to build the pipeline should be easy to qualify or disqualify in your proposed solution.
Plan The Data Workflow
The sequencing of the processes to be used in the data pipeline should be determined at this stage. The workflow defines the sequence of processes, known as tasks, in the data pipeline. At the same time, each task that may be dependent on another within the sequence should be defined in this planning stage. How the steps are planned may be dependent on quite a few factors. A few examples of scenarios which you may need to take into consideration are:
- if a job that performs a specific task can run parallel to other tasks
- If a downstream job outcome will impact an upstream job in the data pipeline processes
- How to handle failed jobs or steps in the data pipeline process
Once the data flow is planned and all scenarios are considered, you can confidently move to the next step.
Implement A Data Monitoring And Governance Framework
A data framework to monitor and provide data governance should be established. This would help in making sure that the data pipeline is healthy, efficient, and reliable at all times. Allocating an individual or an admin to monitor the data is of utmost importance to ensure data integrity. By having a designated admin, you can ensure that your data is secure and meets the requirements set out by your organization. On top of manual monitoring and governance, it may also make sense to deploy tooling around this to automate the process. A good framework should balance automation and manual processes when it comes to data monitoring and governance.
Plan The Data Consumption Layer
The final thing to consider is to think about how the data would be used from the data pipeline to carry out sufficient data analysis. The best ways and methods to retrieve data and how the connection tools would gain access to the data pipeline should be determined. With the data consumption layer in mind, you can make sure that your data pipeline will be delivering data in the correct format to your platform of choice. You should also ensure that the platform used for consumption accommodates the needs of your business and your intended insights. There also may be a consideration where several platforms may be at the receiving end of the data which is being moved through the pipeline.
Flexibility And Scalability Are Important Factors For Sustainable Data Pipelines
The last thing to have at the back of your mind is that it is not just enough to build a data pipeline. Building a data pipeline that can stand the test of time is very important. To be usable for the long term, designing a data pipeline that scales as your data volumes increase should be brought into consideration. The pipeline should be flexible enough to handle most of your data needs as you evolve with only minor tweaks and changes.
Using Arcion To Build Flexible And Scalable Data Pipelines
Building data pipelines don’t have to be complex or take a lot of engineering effort. With Arcion, users can create data pipelines in minutes with no code, minimal effort, and scalability out of the box. On top of these advantages, Arcion offers both self-managed on-premise and fully-managed cloud products to fit your exact needs.
By using Arcion, it’s easy for organizations to build pipelines using many different data sources. Easily move data from a source database to an external system, such as a big data platform like Snowflake or Databricks, with no code required. For many use cases, Arcion is much easier and more flexible than the built-in replication and CDC tools supported by the major database providers, such as Oracle GoldenGate.
Benefits of using Arcion include:
- No-code connector support for 20+ sources and target databases and data warehouses
- Agentless CDC ensures there is zero impact on the source data systems. Arcion reads directly from database logs, never reading from the production database. No need to install a software process on the production system.
- Multiple deployment types supported across cloud and on-premise installations
- Configuration can easily be done through UI, with minimal effort and zero code
- Automatic schema conversion & schema evolution support out-of-the-box (including SQL and NoSQL conversion)
- Patent-pending distributed & highly scalable architecture: Arcion is the only end-to-end multi-threaded CDC solution on the market that auto-scales vertically & horizontally. Any process that Arcion runs on Source & Target is parallelized using patent-pending techniques to achieve maximum throughput.
- Built-in high availability (HA): Arcion is designed with high availability built-in. It makes the pipeline robust without disruption and data is always available in the target, in real-time.
- Auto-recovery (patent-pending): Internally, Arcion does a lot of check-pointing. Therefore, any time the process gets killed for any reason (e.g., database, disk, network, server crashes), it resumes from the point where it was left off, instead of restarting from scratch. The entire process is highly optimized with a novel design that makes the recovery extremely fast.
With Arcion, building data pipelines is easy to do and pipelines built with Arcion are easy to maintain. Arcion can help apply many of the steps we outlined above with ease and some even with automation, such as SQL to NoSQL transformations.
In this article, we discussed what a data pipeline is and highlighted its importance as the foundation for digital systems. We discussed how data pipelines involve the movement, transformation, and storage of data from which organizations can gain critical insights. It allows data teams to make faster and more reliable decisions as data is brought from various data sources into a single data repository.
We also discussed important factors to consider before you start building a data pipeline. These factors include understanding business objectives, defining your data sources and destinations, and determining the transformation process data may need to go through. We also discussed the importance of using the right tools to build robust and scalable pipelines. One such tool we discussed is Arcion and the ease with which it can be used to create data pipelines that are flexible, easy to manage, and infinitely scalable. To try out Arcion for yourself, you can download Arcion for free and access all features & connectors. What’s better is that we don’t ask for any payment info so it’s 100% risk free.
With the info covered in this article, we hope that you’ll be able to make informed decisions as you begin creating your own data pipelines to power the data and business initiatives your business has set forward.