Data Pipeline Design: From Ingestion To Analytics
Using data pipelines, raw data is transported from software-as-a-service platforms and database sources to data warehouses, where it may be used by analytics and business intelligence (BI) tools to make decisions. It is possible for developers to create their own data pipelines by writing code and manually connecting with source databases; however, it is preferable to avoid reinventing the wheel and instead use a SaaS data pipeline.
Let’s take a look at the core components and phases of data pipelines, as well as the technologies available for duplicating data, to get a sense of how big of a revolution data pipeline-as-a-service is, and how much work goes into building an old-school data pipeline.
The architecture of the data pipeline
A data pipeline architecture is the design and structure of code and systems that copy, cleanse, and modify source data as needed, and then route it to destination systems such as data warehouses and data lakes, among other things.
A data pipeline’s processing speed is influenced by three factors: the amount of data being processed, the amount of data being moved, and how much data is being moved.
- The rate, also known as throughput, of a pipeline, refers to how much data it can handle in a given length of time.
- Reliability: Individual systems within a data pipeline must be fault-tolerant in order for the data pipeline to function reliably. It is possible to assure data quality by using a dependable data pipeline that includes built-in auditing, logging, and validation procedures.
- Latency is defined as the amount of time it takes for a single unit of data to transit through a data pipeline. While latency is related to reaction time, it is less related to volume or throughput. When it comes to both pricing and processing resources, maintaining low latency may be a costly endeavour, and a company should find a balance in order to optimise the value it derives from analytics.
Data engineers should strive to make these elements of the pipeline more efficient in order to meet the demands of the company. When designing a pipeline, an enterprise must take into account its business objectives, the cost of the pipeline, as well as the type and availability of computational resources.
Building a data pipeline is a challenging task.
The architecture of the data pipeline is tiered. After that, each subsystem feeds data into the one before it reaches its target.
Sources of information
Considering that we’re talking about pipelines, we may think of data sources as the wells, lakes, and streams from which companies get their initial batch of information. Thousands of possible data sources are supported by SaaS providers, and every business maintains dozens of others on its own systems. Data sources are critical to the design of a data pipeline since they are the initial layer in the pipeline. There is nothing to ingest and move through the pipeline if the data is not of high quality.
As illustrated by our plumbing metaphor, the data pipeline’s ingestion components consist of operations that read data from data sources (i.e., the pumps and aqueducts). Extractions are performed on each data source using application programming interfaces (API) that are supplied by the data source. Before you can develop code that uses APIs, however, you must first determine what data you want to extract through a process known as data profiling. Data profiling is the process of assessing data for its features and structure, as well as evaluating how well it meets a business objective.
After the data has been profiled, it is ingested into the system, either in batches or in real time.
Batch ingestion and streaming ingestion
Batch processing is the process of extracting and operating on groups of records at the same time. Batch processing is sequential, and the ingestion mechanism reads, processes, and outputs groups of information based on criteria established in advance by developers and analysts. Batch processing is also known as sequential processing. The process does not continuously monitor for new records and move them forward in real-time, but rather operates on a timetable or responds in response to external events instead.
Streaming is an alternate data ingestion paradigm in which data sources automatically send individual records or units of information one at a time to the receiving system. Batch ingestion is used by all organisations for a wide variety of data types, but streaming ingestion is only used by businesses when they want near-real-time data for usage with applications or analytics that require the least amount of delay at the lowest feasible cost.
Depending on the data transformation requirements of a business, the data is either transferred into a staging area or delivered immediately along the flow path.
Once data has been retrieved from source systems, it may be necessary to modify the data’s structure or format. Desalination stations, treatment plants, and personal water filters are the desalination stations, treatment plants, and personal water filters of the data pipeline.
Mapped values to more descriptive ones, filtering, and aggregation are all examples of transformations in data management. Combination is a particularly significant sort of transformation since it allows for more complex transformations. Included in this category are database joins, which take use of the relationships inherent in relational data models to bring together linked multiple tables, columns, and records in a single place.
Whether a company uses ETL (extract, transform, load) or ELT (extract, load, transform) as its data replication method in its data pipeline determines the time of any transformations (extract, load, transform). Early transactional load (ETL), an older technique that is still employed with on-premises data warehouses, can modify data before it is put into its intended destination. ELT is a data loading technique that may be used with contemporary cloud-based data warehouses to import data without doing any transformations. Users of data warehouses and data lakes can then perform their own transformations on the data contained within the warehouse or data lake.
The data pipeline’s water towers and storage tanks serve as destinations. The primary destination for data repeated via the pipeline is a data warehouse. These specialist databases house all of an enterprise’s cleansed, mastered data in a single location for analysts and executives to utilise in analytics, reporting, and business intelligence.
Less-structured data may be fed into data lakes, where data analysts and data scientists can access massive amounts of rich and mineable information.
Finally, an organisation may input data into an analytics application or service that takes data feeds directly.
Data pipelines are complicated systems made up of software, hardware, and networking components, any of which might fail. Developers must build monitoring, logging, and alerting code to enable data engineers to maintain performance and fix any problems that emerge in order to keep the pipeline operational and capable of extracting and loading data.
Technologies and strategies for data pipelines
Businesses have two options when it comes to data pipelines: create their own or utilise a SaaS pipeline.
Organizations can delegate to their developers the responsibility of creating, testing, and maintaining the code necessary for a data pipeline. Several toolkits and frameworks may be used throughout the process:
- Workflow management solutions can make it easier to create a data pipeline. Open-source technologies like Airflow and Luigi structure the pipeline’s operations, automatically resolve dependencies and allow developers to analyse and organise data workflows.
- Event and messaging frameworks such as Apache Kafka and RabbitMQ enable organisations to create more timely and accurate data from their current systems. These frameworks gather events from business applications and make them available as high-throughput streams, allowing disparate systems to communicate using their own protocols.
- Process scheduling is also important in any data pipeline. Many technologies, ranging from the basic cron utility to full specialised task automation systems, allow users to define comprehensive schedules regulating data intake, transformation, and loading to destinations.
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