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HomeBlogCloud ComputingWhat is a Data Pipeline? Usage, Types, and Applications
Every business these days is looking for ways to integrate data from multiple sources to gain business insights for a competitive advantage or to form an image in the current market.
Organizations and individuals can achieve a few of their goals based on outcomes that are generated with the help of a data pipeline. Suppose you want daily sales data from a point-of-sale system from a retail outlet so that you can find the total sales of a day and the data get extracted from a series of processes which is usually done via Data Pipeline.
Data Pipeline is a flow of process/mechanism used for moving data from one source to destination through some intermediatory steps. Filtering and features that offer resilience against failure may also be included in a pipeline.
In simple terms, let us go with one analogy, consider a pipe that accepts input from a source and transports it to supply output at the destination. The data pipeline use cases change with business requirements.
A data pipeline is a crucial instrument for gathering data for enterprises. To assess user behavior and other information, this raw data may be gathered. The data is effectively kept at a location for current or future analysis with the use of a data pipeline.
When data is routinely gathered, converted, and sent to a cloud data warehouse for business operations and traditional business intelligence use cases, a batch process is typically used. Users may easily plan the jobs for processing massive volumes of data with little to no human involvement from siloed sources into a cloud data lake or data warehouse.
Using a high-throughput messaging system, streaming data pipelines allow users to ingest structured and unstructured data from a variety of streaming sources, such as the Internet of Things (IoT), connected devices, social media feeds, sensor data, and mobile applications, while ensuring that the data is accurately recorded.
Let us examine this distinction with the aid of a household analogy. Consider this as water supply infrastructure where we have water resources (large amount of water) then it is moved to treatment plant for water treatment. The treated water is then moved in storage (warehouse). The stored water is then sent to houses for their daily use.
Same is in the case of Data Pipeline, the enormous amount of data is collected first and then moved for data quality where the useful amount of data is extracted. The extracted data is then sent to various businesses for their research purposes.
This water supply infrastructure example serves as a nice analogy for explaining how the data pipeline works and related to it:
The design and organization of software and systems that copy, purge, or convert data as necessary and then route it to target systems like data warehouses and data lakes is known as Data pipeline architecture. Data pipelines consist of three essential elements which define its architecture:
1. Data Sources
Data are gathered from sources. A few relational database management systems like MySQL, Customer relationship management tools like HubSpot and Salesforce, Enterprise Resource Planning systems like Oracle or SAP, some search engine tools, and even IoT device sensors which includes speedometers as well are examples of common sources of gathering the data.
2. Preparation
Data is typically taken from sources, altered, and changed following business requirements, and then placed at its destination. Transformation, augmentation, filtering, grouping, and aggregation are typical processing processes.
3. Destination
When data processing is complete, it goes to a destination, usually a data warehouse or data lake for analysis.
In many terms, you can say that data pipeline is super set of ETL and that there is no comparison between them. ETL stands for Extract, Transform, and Load which is a subset of Data Pipeline. ETL refers to a collection of operations that take data from one system, transform it, and load it into another. A data pipeline is a broader phrase that refers to any set of procedures that transports data from one system to another, whether it is transformed or not.
Data from sources including business processes, event tracking systems, and data banks are sent into a data warehouse for business intelligence and analytics via a data pipeline. In contrast, an ETL pipeline loads the data into the target system after it has been extracted, converted, and loaded. The order is crucial; after obtaining data from the source, you must incorporate it into a data model that has been created following your business intelligence needs. This is carried out by gathering, purifying, and then altering the data. The last step is to import the output data into your ETL data warehouse.
Despite being synonymous with each other, ETL and data pipelines are two distinct concepts. Data pipeline tools may or may not have data transformation, while ETL tools are used for data extraction, transformation, and loading.
Structured data is information that adheres to a predefined manner or model. This type of data can be quickly analyzed. Unstructured data is gathered information that is not organized in a predefined way or model. This type of unstructured data is very text heavy as it holds facts, dates, and numbers. With its irregularities, this information is difficult to understand compared to data in a fielded database.
Raw data is information that has not been processed for any particular purpose. This data is also known as primary data, and it can include figures, numbers, and readings. The raw data is collected from various sources and moved to a location for analysis or storage.
Processed data comes from collected raw data. System processes convert the raw data into a format for easier visualization or analysis. These processes can also clean and transform the processed data into the desired location.
Cooked data is another type of raw data that has gone through the processing system. During processing, the raw data has been extracted and organized. In some cases, it has been analyzed and stored for future use.
The environment for gathering and analyzing data has recently undergone tremendous change. The main goal of creating data pipelines was to transfer data from one layer (transactional or event sources) to data lakes or warehouses where insights might be extracted.
Data pipeline's origins may be traced back to the days when we manually updated tables using data input operators. There were many human errors in it, making regular data uploads without human interaction necessary, especially for sensitive data from institutions like company which manufactured products, banks, and insurance firms.
To guarantee that the data was available the next day, transactional data used to be posted every evening. This method was more practical, so gradually we transitioned to the intervals that could be set for these data transfers. Even for urgent purchases, consumers still had to wait until the next day.
Data must be efficiently transferred as soon as feasible from one location to another and transformed into usable information for data-driven organizations. Sadly, there are several barriers to clear data corruption, data flow, including bottlenecks (which cause delay), and various data sources that provide duplicate or contradictory data.
The human procedures needed to address those issues are all eliminated by data pipelines, which transform the procedure into an efficient, automated workflow.
Although there are many distinct types of data pipelining tools and solutions, they all must meet the same three criteria:
Kafka may also distribute and replicate partitions over several servers, preventing server failure. Companies can use real-time analytics to receive up-to-date information about operations and respond quickly, or to supply solutions for smart monitoring of infrastructure performance.
Data quality pipelines supply features like frequent standardization of all new client names. Real-time customer address validation during the acceptance of a credit application would be regarded as a part of a data quality pipeline.
Data matching and merging are key components of master data management (MDM). In this pipeline, data is gathered and processed from many sources, duplicate records are found, and the results are combined into a single golden record.
Complex structured or unstructured documents, such as EDI and NACHA documents, SWIFT transactions, and HIPAA transactions, can be sent and received by enterprises from other businesses. B2B data exchange pipelines are used by businesses to send documents like purchase orders or shipment statuses.
Cloud-based data pipeline solution, i.e., AWS Data Pipeline enables you to process and transfer data between various AWS services and on-premises data sources. You can use the web service AWS Data Pipeline to automate the transfer and transformation of data. Also, it is possible to create data-driven workflows with AWS Data Pipeline so that tasks are dependent on the accomplishment of earlier actions. Your data transformations' parameters are defined by you, and AWS Data Pipeline upholds the logic you have set up.
To set up AWS Data Pipeline in detail one must have some knowledge of AWS solution architect. One can get training on AWS Solution architect associate do refer to AWS Solution Architect Curriculum.
To better see and deal with data, users rely on conventional data preparation tools like spreadsheets. Unfortunately, this also requires users to manually manage each new dataset or develop intricate macros. Thank goodness, there exist technologies for business data preparation that can transform manual data preparation procedures into automated data pipelines.
With the help of an intuitive interface, you may use tools created to construct data processing pipelines using the digital equivalent of toy building blocks.
Users use SQL, Spark, Kafka, MapReduce, and other languages and frameworks for data processing. AWS Glue and Databricks Spark are two examples of proprietary frameworks that you may also use. Users must have programming knowledge to apply this strategy.
Finally, you must decide which data pipelining design pattern best suits your requirements and put it into practice. They consist of:
This straightforward implementation transfers massive, unchanged data between databases.
Before being loaded into the target database, this approach pulls data from a data store and helps clean, standardize, and integrate.
This concept is like ETL; however, the processes have been altered to reduce latency and save time. The target database is where the data is transformed.
Virtualization offers the data as views without physically keeping a separate copy, in contrast to typical processes that make physical copies of stored data.
This method continuously streams event data in a chronological order. The method separates events, separating each different occurrence into a separate record, allowing assessment for a later use.
The following stages make up the essential process: sources of data and their generation:
In terms of data pipeline there are several terms that can match the requirements of Data Science. Let us look at some of these terms below:
Data Science is a vastly intricate and sophisticated discipline. These are only a few of the terms you will often hear while discussing Data Science, and they only serve as a high-level overview of the subject.
There are several data pipelines that use standard procedures like:
Movement will be done by:
We hope that this article helped you understand Data Pipeline and aided you in navigating broader in the field of Data. Your comprehension of data pipelines has been thoroughly explained in this article. If you want to learn more about Data Pipeline and other technical courses including Cloud Computing or Data Science, check out KnowledgeHut Computing Certification course where you will learn everything needed to become a professional in any technology.
A data pipeline automates data transformation and transfer between a source system and a destination repository by using several data-related technologies and methods.
Assume you are the owner of an eCommerce company and want to successfully personalize your offerings or use data for rapid insights. For jobs like reporting, business intelligence, sentiment analysis, and recommendation systems, you will therefore need to develop a lot of pipelines.
Steps to follow for data pipeline are:
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