Most modern businesses make use of a large number of platforms to smoothly run their day-to-day operations. This is a result of the developments in Cloud-based technologies. Data Pipelines make it possible for companies to access data on Cloud platforms. But What is Data Pipeline? and what does it do? For example, the Marketing team might be using a combination of Marketo and HubSpot for Marketing Automation, whereas the Sales team might be leveraging Salesforce to manage leads, and the Product team might be using MySQL to store customer insights.
This would lead to the fragmentation of data across numerous tools and result in the formation of Data Silos. As a result, there is no single location where all data is present and cannot be accessed if required. Data Silos can make it extremely difficult for businesses to fetch even simple business insights.
Hence, there is a need for a robust mechanism that can consolidate data from various sources automatically into one common destination. This data can then be used for further analysis or to transfer to other Cloud or On-premise systems.
This article will provide you with a comprehensive understanding of what is Data Pipeline, what its components and key types are, and the various architectures that are implemented to create Data Pipelines.
Table of Contents
- What is Data Pipeline?
- Data Pipeline vs ETL
- Types of Data Pipelines
- Components of a Data Pipeline
- Benefits of a Data Pipeline
- Examples of Data Pipeline Architectures
What is Data Pipeline?
A Data Pipeline can be defined as a series of steps implemented in a specific order to process data and transfer it from one system to another. The first step in a Data Pipeline involves extracting data from the source as input. The output generated at each step acts as the input for the next step. This process continues until the pipeline is completely executed. In addition, some independent steps might run in parallel as well in some cases.
Data Pipelines usually consist of three main elements, i.e., a data source, processing steps, and a final destination or sink. Data Pipelines give users the ability to transfer data from a source to a destination and make some modifications to it during the transfer process. Data Pipelines may also have the same source and destination, with the Data Pipeline only being used to transform the data as per requirements.
However, the variety, volume, and velocity of data have changed drastically and become more complex in recent years. Hence, Data Pipelines now have to be powerful enough to handle the Big Data requirements of most businesses. It is of paramount importance to businesses that their pipelines have no data loss and can ensure high accuracy since the high volume of data can open opportunities for operations such as Real-time Reporting, Predictive Analytics, etc.
Big Data Pipelines are built to accommodate all three traits of Big Data, i.e., Velocity, Volume, and Variety. The velocity with which data is generated means that pipelines should be able to handle Streaming Data. This data has to be processed in real-time by the pipeline. The volume of the generated can vary with time which means that pipelines must be scalable. The Data Pipelines should be able to accommodate all possible varieties of data, i.e., Structured, Semi-structured, or Unstructured.
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- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Data Pipeline vs ETL
ETL and Data Pipeline are terms that are often used interchangeably. ETL stands for Extract, Transform, and Load. ETL pipelines are primarily used to extract data from a source system, transform it based on requirements and load it into a Database or Data Warehouse, primarily for Analytical purposes.
However, Data Pipeline can be seen as a broader term that encompasses ETL as a subset. It refers to a system that is used for moving data from one system to another. This data may or may not go through any transformations. It may be processed in batches or in real-time; based on business and data requirements. This data might be loaded onto multiple destinations, such as an AWS S3 Bucket or a Data Lake, or it might even be used to trigger a Webhook on a different system to start a specific business process.
Types of Data Pipelines
Now that you have understood what is Data Pipeline and ETL. Lets’s read about different Data Pipelines. The four types of Data Pipelines are as follows:
- Batch: Batch processing of data is leveraged when businesses want to move high volumes of data at regular intervals. Batch processing jobs will typically run on a fixed schedule (for example, every 24 hours), or in some cases, once the volume of data reaches a specific threshold.
- Real-time: Real-time Data Pipelines are optimized to process the necessary data in real-time, i.e., as soon as it is generated at the source. Real-time processing is useful when processing data from a streaming source, such as the data from financial markets or telemetry from connected devices.
- Cloud-native: These pipelines are optimized to work only with Cloud-based data sources, destinations, or both. These pipelines are hosted directly in the Cloud, allowing businesses to save money on infrastructure and expert resources.
- Open-source: These pipelines are considered to be suitable for businesses that need a low-cost alternative to commercial pipelines or wish to develop a pipeline to fit their unique business and data requirements. However, these pipelines require the support of trained professionals for their development and maintenance.
However, it is important to understand that these types are not mutually exclusive. This means that a Data Pipeline can have all characteristics of two different types. For example, Data Pipelines can be Cloud-native Batch Processing or Open-Source Real-time processing, etc.
Components of a Data Pipeline
The components of a Data Pipeline are as follows:
- Origin: Origin is the point of entry for data from all data sources in the pipeline. Most pipelines have transactional processing applications, application APIs, IoT device sensors, etc., or storage systems such as Data Warehouses, Data Lakes, etc. as their origin.
- Destination: This is the final point to which data is transferred. The final destination depends on the use case. The destination is a Data Warehouse, Data Lake, or Data Analysis and Business Intelligence tool for most use cases.
- Dataflow: This refers to the movement of data from origin to destination, along with the transformations that are performed on it. One of the most widely used approaches to data flow is called ETL (Extract, Transform, Load). The three phases in ETL are as follows:
- Extract: Extraction can be defined as the process of gathering all essential data from the source systems. For most ETL processes, these sources can be Databases such as MySQL, MongoDB, Oracle, etc., Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) tools, or various other files, documents, web pages, etc.
- Transform: Transformation can be defined as the process of converting the data into a format suitable for analysis such that it can be easily understood by a Business Intelligence or Data Analysis tool. The following operations are usually performed in this phase:
- Filtering, de-duplicating, cleansing, validating, and authenticating the data.
- Performing all necessary translations, calculations, or summarizations on the extracted raw data. This can include operations such as changing row and column headers for consistency, standardizing data types, and many others to suit the organization’s specific Business Intelligence (BI) and Data Analysis requirements.
- Encrypting, removing, or hiding data governed by industry or government regulations.
- Formatting the data into tables and performing the necessary joins to match the Schema of the destination Data Warehouse.
- Load: Loading can be defined as the process of storing the transformed data in the destination of choice, normally a Data Warehouse such as Amazon Redshift, Google BigQuery, Snowflake, etc.
- Storage: Storage refers to all systems that are leveraged to preserve data at different stages as it progresses through the pipeline.
- Processing: Processing includes all activities and steps for ingesting data from sources, storing it, transforming, and loading it into the destination. While data processing is associated with the data flow, the focus in this step is on the implementation of the data flow.
- Workflow: Workflow defines a sequence of processes along with their dependency on each other in the Data Pipeline.
- Monitoring: The goal of monitoring is to ensure that the Data Pipeline and all its stages are working correctly and performing the required operations.
- Technology: These are the infrastructure and tools behind Data Flow, Processing, Storage, Workflow, and Monitoring. Some of the tools and technologies that can help build efficient Data Pipelines are as follows:
- ETL tools: Tools used for Data Integration and Data Preparation, such as Hevo, Informatica PowerCenter, Talend Open Studio, Apache Spark, etc.
- Data Warehouses: Central repositories that are used for storing historical and relational data. A common use case for Data Warehouses is Business Intelligence. Examples of Data Warehouses include Amazon Redshift, Google BigQuery, etc.
- Data Lakes: Data Lakes are used for storing raw Relational or Non-relational data. A common use case for Data Lakes in Machine Learning applications being implemented by Data Scientists. Examples of Data Lakes include IBM Data Lake, MongoDB Atlas Data Lake, etc.
- Batch Workflow Schedulers: These schedulers give users the ability to programmatically specify workflows as tasks with dependencies between them to automate and monitor these workflows. Examples of Batch Workflow Schedulers include Luigi, Airflow, Azkaban, Oozie, etc.
- Streaming Data Processing Tools: These tools are used to handle data that is continuously generated by sources and has to be processed as soon as it is generated. Examples of Streaming Data Processing tools include Flink, Apache Spark, Apache Kafka, etc.
- Programming Languages: These are used to define pipeline processes as code. Python and Java are widely used to create Data Pipelines.
Benefits of a Data Pipeline
When companies don’t know about what is Data Pipeline, they used to manage their data in an unstructured and unreliable way. But as they came to know about What is Data Pipeline and how it helps companies save time and keep their data organized always. A few benefits of Data Pipeline are listed below:
- Data Quality: The data flows from source to destination can be easily monitored and accessible, and meaningful to the end-users.
- Incremental Build: Data Pipelines allow users to create dataflows incrementally. You can pull even a small slice of data from the data source to the user.
- Replicable Patterns: Data Pipelines can be reused and repurposed for new data flows. They are a network of pipelines that creates a way of thinking that sees individual Data Pipelines as examples of patterns in a wider architecture.
Examples of Data Pipeline Architectures
Some examples of the most widely used Data Pipeline Architectures are as follows:
- Batch-based Pipeline: In this example, you have a data source which might be an application such as a point-of-sale system that generates numerous data points that need to be pushed to a Data Warehouse or any other Analytics database. The pipeline for this use case will have the following architecture:
- Stream-based Pipeline: In this example, data from the source system has to be processed as it is generated. The Stream Processing engine in this pipeline will feed outputs from the pipeline to Data Stores, Customer Relationship Management (CRM) systems, Marketing applications, etc. A Stream-based Pipeline will have the following architecture:
- Lambda Pipeline: This pipeline is a combination of Batch and Streaming Data Pipelines. This architecture is widely used in Big Data environments as it gives developers the ability to account for historical Batch analysis and Real-time Streaming use cases. A vital aspect of this architecture is that it encourages storing data in a raw form, allowing developers to run new Data Pipelines to correct any errors in prior pipelines or integrate new data destinations as per the use case. The following image shows the Lambda Architecture:
This article provided you with a comprehensive understanding of what is Data Pipeline are. It also helped you understand the fundamental types and components of most modern Data Pipelines.
Most businesses today, however, have an extremely high volume of data with a dynamic structure. Creating a Data Pipeline from scratch for such data is a complex process since businesses will have to utilize a high amount of resources to develop it and then ensure that it can keep up with the increased data volume and Schema variations. Businesses can instead use automated platforms like Hevo.Visit our Website to Explore Hevo
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