Processing...
Δ
In today’s business world, the importance of data cannot be overstated. But the ever-growing amount of information makes it difficult to handle all the incoming data and utilize it in the most meaningful way. How does one process these vast amounts of data and bring big data to a consolidated form? In this article, we will dive into the data pipeline and its main features and investigate how to build a reliable pipeline and avoid common early-stage mistakes.
A data pipeline refers to the process of ingesting raw data from various sources, filtering it, and finally moving it to a destination for storing or analyzing. The process includes a series of steps that incorporate three main elements: a source, processing steps, and a destination. For example, you may move data from an application to a data lake or data warehouse, or from data storage to an analytics tool.
Data pipelines are becoming more meaningful in environments where microservices are sought after. Microservices are applications with small codebases, and they move data between various applications to make the output more efficient. One data pipeline example could be a social media post that could potentially fuel a social listening report, a sentiment analysis, and a marketing app that counts brand mentions. Even though the data source is singular, it can feed different pipelines and bring out helpful insights.
Data pipeline processes vary from business case to business case. It may be a straightforward process of data extraction and loading or it could be a complex data flow in which data is processed for the purpose of training machine learning models. The process consists of the following elements:
Data sources comprise relational databases and SaaS applications whereby the data is pulled from multiple sources via an API call, webhook, or a push mechanism. Depending on the business case, data can be moved in real-time or at certain intervals that are relevant to the concrete goal. Businesses can choose between various platforms like AWS S3, Google Bucket, and Azure blob storage delivered by the cloud service provider of their preference.
When it comes to processing, there are two main data pipeline architectures: batch processing, which involves gathering data at certain intervals and wiring it to the destination, and stream processing, which sends data as soon as it is generated. There is a third data pipeline example that combines both aforementioned models. Lambda is a pipeline component that facilitates real-time processing while providing a platform to store and access historical batch data. This way, developers can create additional pipelines to correct errors in previous pipelines or wire them to new destinations if a new business case emerges.
Data transformation brings datasets to a format that is required for a certain business goal. This step involves standardizing datasets, sorting them out, removing duplicates, and validating and verifying information. Another vital aspect of data transformation is normalization and denormalization where the former refers to removing data redundancy and the latter combines data into a single source. The purpose of this step is to pave the way to further analyze datasets in the most efficient way.
Destination varies depending on the logic of the data pipeline. In some cases, it may land in a data lake, data warehouse, or data mart while in others it can go to analytics tools. For example, platforms like Google Big Query, AWS Glue, Lambda, AWS EMR, Azure Bus transfer, etc. are commonly used for analytic purposes.
Monitoring is an essential part of a well-functioning data pipeline as it adds quality assurance to the process. Administrators must be notified when something goes wrong that can jeopardize data integrity. For example, a source can go offline and stop fueling the pipeline with new information. The most prominent monitoring tools are Looker and Tableau.
An efficient data pipeline needs to encompass a list of features that make the team more productive and the results more viable.
Cloud-based solutions simplify and optimize Big Data ETL setup and management and allow companies to focus on business goals rather than technical tasks. Cloud providers carry the responsibility for the rollout and support of services and architectures and maintain their efficiency.
Market trends change rapidly, and the need for real-time data processing is becoming more critical. Modern data pipelines collect, transform, process, and move data in real-time, which allows companies to have access to the most relevant and recent information. On the other side of the spectrum is batch processing, which delivers data with a few hours or even days of delay. This can be hazardous to the business as trends change incredibly quickly. Real-time data processing offers a competitive edge and helps businesses make data-driven decisions—especially businesses that rely on real-time data, such as logistics firms who are required to adopt this type of processing power.
Fault-tolerant architecture refers to an automatic backup plan that turns on another node in case the original one fails. This feature protects businesses from unexpected failures and substantial monetary losses. These types of pipelines offer more reliability and availability and guarantee stable and smooth processing.
The amount of data generated on a daily basis is enormous and unimaginable, and 80% of this data is unstructured or semi-structured. An efficient data pipeline needs to have the capacity to process large data volumes of unstructured data, such as sensor or weather data, as well as semi-structured data such as JSON, XML and HTML. On top of that, a pipeline has to be able to transform, clean, filter, and aggregate datasets in real-time, which requires significant amounts of processing power.
DataOps is a set of methodologies and principles that helps developers shorten the development cycle. It does so by automating the processes across the entire lifecycle. In the long run, such pipelines are easier to scale up and down, run tests with, and modify in the pre-deployment phase.
Data democratization opens up access to the data across the company and removes gatekeeping that hinders the development of the business. Instead of only a few executives having access to important information, everyone that works at the company can analyze the data and use it for their decision-making.
In this section, we will discuss the most common data pipeline tools that are currently used by companies.
FOSS software offers transparency and open-sourceness that helps companies build pipelines even on a low budget. Among the most popular and prominent solutions are pandas, Apache Airflow, Postgres and Metabase, Spark, DASK, VAEX, Apache Kafka
A segment is one of the best data pipeline tools that is centered around the customer. The platform is known for its clickable user interface and helps businesses create and manage routes between sources and destinations.
Keboola is one of the most holistic and advanced data pipeline tools that cover all the bases— including ETL jobs, monitoring, data management, and even machine learning features. The software also provides customization options through its plug-and-play design.
Fivetran is a platform that automates ETL tasks and connects data sources to destinations. The list of data sources is vast and even includes data warehouses but not data lakes.
Xplenty is a data pipeline platform that comes with a drag-and-drop interface and connects data sources to destinations.
When it comes to data, a lot of things can go wrong, and identifying mistakes in the middle of the functioning pipeline takes immense amounts of resources. Using a set of data pipeline best practices will help you avoid pitfalls in the early stages and prepare rollout plans to catch and solve issues as soon as possible.
We have already discussed the different data pipeline architectures that deliver the results at a given pace. It is crucial to decide whether you need a batch or real-time processing early on. If your data sources generate data on a daily basis, then there is no point in investing time and money in building a real-time data pipeline. The costs of it are much higher than a batch data pipeline and the time it takes to get it running would also not be worth it. If you decide that real-time processing is essential to your business, then this needs to be established at the very beginning of the development.
When transforming and moving data along the pipeline, make sure to keep the original raw data in storage. This best practice will help you to go back and reprocess the data in case a new business agenda comes up. You can also create new pipelines with this data and wire it to different destinations in the future. Raw, unstructured data can be stored using Azure, S3, or on-premises.
Transformation encompasses a range of tasks such as validation, verification, enrichment, cleaning, sorting, categorizing, and more. Much like any other software development process, this step needs to be broken down into smaller, more manageable subtasks that are easier to process and test. Moreover, the tasks need to follow the same schema each time you run the pipeline. In other words, if you go through the transformation process again, the results must be identical. This best practice simplifies the testing, enhances the quality of data as well as makes the pipeline easier to manage and maintain. If something needs to be added to the transformation step, you can be sure that it will only affect the changes you made.
Especially relevant for mature pipelines, it is vital to allow yourself an opportunity to backfill the stored data to correct a faulty pipeline. At the beginning of the development cycle, you might not realize an unusual pattern or dismiss it as unimportant. However, a few days or weeks later you might recognize the mistake and need to ingest more data into the pipeline. Also, make sure to leave the ability to refill data for a particular period to preserve the rest of the timeline data.
Here is the question of how to build a data pipeline in the most efficient way that guarantees the quality and security of the data. The answer is by working in small iterations. The first iteration involves creating a single source of data, basic processing, and accessible storage. The first run should remain simple to dig the initial path to data and identify possible bottlenecks. Besides that, the initial iteration includes creating a cloud environment, setting up the continuous development (CD) pipeline, launching a test framework, modeling data, educating users on data access, and adding monitoring options. Later iterations expand on data sources, processing and transformation functions, advanced monitoring, and more.
The process of building and deploying a data pipeline is complex and lengthy and requires technical know-how. On top of that, you need to understand business goals, identify relevant data sources and corresponding destinations, and select the right tools. If you would like expert help to set up a reliable pipeline, you can get in touch with NIX United. Our team of professionals will support you throughout the entire process and make sure your data pipeline is well-functioning and meaningful to your business objectives.
Be the first to get blog updates and NIX news!
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
SHARE THIS ARTICLE:
Schedule Meeting
