Data pipeline is a method in which raw data is ingested from various data sources and then ported to data store, like data lake or data warehouse, for analysis. A data pipeline is a means of moving data from one place (the source) to a destination (such as data warehouse). Along the way, data is transformed and optimized, arriving in a state that can be analyzed and used to develop business insights. In relation, a pipeline specifies the business logic of your data management. It encompasses schedules and run tasks for instances to perform the defined work activities. In brief, a data pipeline is a set of actions that ingest raw data from disparate sources and move the data to a destination for storage and analysis.
Benefits of a Data Pipeline
Your organization likely deals with massive amounts of data. To analyze all of that data, you need a single view of the entire data set. When that data resides in multiple systems and services, it needs to be combined in ways that make sense for in-depth analysis. Data flow itself can be unreliable: there are many points during the transport from one system to another where corruption or bottlenecks can occur. As the breadth and scope of the role data plays increases, the problems only get magnified in scale and impact. That is why data pipelines are critical. They eliminate most manual steps from the process and enable a smooth, automated flow of data from one stage to another. They're essential for real-time analytics to help you make faster, data-driven decisions.
They're important if your organization:
1. Relies on real-time data analysis.
2. Stores data in the cloud.
3. Houses data in multiple sources. By consolidating data from your various silos (i.e., a tall tower or pit on a farm used to store grain) into one single source of truth, you're ensuring consistent data quality and enabling quick data analysis for business insights.
Elements of a Data Pipeline
Data pipelines consist of three essential elements:
1. A source or sources,
2. Processing steps,
3. Destination
1. Sources
Sources are where data comes from. Common sources include relational database management systems like MySQL, CRMs such as Salesforce and HubSpot, ERPs like SAP and Oracle, social media management tools, and even IoT device sensors.
2. Processing steps
In general, data is extracted data from sources, manipulated and changed according to business needs, and then deposited it at its destination. Common processing steps including transformation, augmentation, filtering, grouping, and aggregation.
3. Destination
A destination is where the data arrives at the need of its processing, typically a data lake or data warehouse for analysis.
What are the components of a Data Pipeline?
The components of a pipeline are as follows:
1. 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.
2. 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.
3. 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 ELT (Extract, Transform, Load). The three phases in ETL are as follows:
A. 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.
B. 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:
1. Filtering, de-duplicating, cleansing, validating, and authenticating the data.
2. 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.
3. Encrypting, removing, or hiding data governed by industry or government regulations.
4. Formatting the data into tables and performing the necessary joins to match the Schema of the destination Data Warehouse.
3. 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.
4. Storage: Storage refers to all systems that are leveraged to preserve data at different stages as it progresses through the pipeline.
5. 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.
6. Workflow: Workflow defines a sequence of processes along with their dependency on each other in the Pipeline.
7. Monitoring: The goal of monitoring is to ensure that the Pipeline and all its stages are working correctly and performing the required operations.
8. 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 Pipelines are as follows:
1. ELT tools: Tools used for Data Integration and Data Preparation, such as Hevo, Informatica PowerCenter, Talend Open Studio, Apache Spark, etc.
2. 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.
3. Data Lakes: they're used for storing raw Relational or Non-relational data. A common use case for Data Lakes in ML apps being implemented by Data Scientists. Examples of Data Lakes include IBM Data Lake, MongoDB Atlas Data Lake, etc.
9. 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.
10. 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.
11. Programming Languages: These are used to define pipeline processes as code. Python and Java are widely used to create Pipelines.
Data Pipeline Architecture
Data Pipeline architecture describes the exact arrangement of components to enable the extraction, processing, and delivery of information. There are several common design business can consider.
ETL Data Pipeline
ETL is the most common data pipeline architecture, one that has been a standard for decades. It extracts raw data from disparate sources, transforms it into a single pre-defined format, and loads it into a target system - typically, an enterprise-data-warehouse or data mart.
Typical use cases for ELT pipelines include:
1. Data migration from legacy systems to a data warehouse.
2. Pulling user data from multiple touchpoints to have all information on customers in one place (usually, in CRM system).
3. Consolidating high volumes of data from different types of internal and external sources to provide a holistic view of business operations, and
4. joining disparate datasets to enable deeper analytics
The key downside of the ELT architecture is that you have to rebuild your data pipeline each time business rules (and requirements for data formats) change. To address this problem, another approach to data pipeline architecture - ELT - appeared.
ELT Data Pipeline
ELT differs from ETL in the sequence of steps: loading happens before the transformation. This seemingly minor shift changes a lot. Instead of converting huge amounts of raw data, you first move it directly into a data warehouse or data lake. Then, you can process and structure your data as needed - at any moment, fully or partially, once or numerous times.
ELT architecture comes in handy when:
1. you're not sure what you're going to do with data and how exactly you want to transform it;
2. the speed of data ingestion plays a key role; and
3. huge amounts of data are involved
Yet, ELT is still a less mature technology than ETL which creates problems in terms of available tools and talent pool. You can use either ETL or ELT architecture or a combination of the two as a basis for building a data pipeline for traditional or real-time analytics.
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