Considering the explosion of IoT solutions and cloud computing, it is expected that the global data volume will skyrocket to 175 zettabytes in 2025, a staggering increase from 33 zettabytes in 2018! Sadly, less than 0.5% of people actually engage with and utilize any of the data. The task of processing enormous amounts of data into real business tasks has one extremely advanced solution. This conundrum can be overcome by Data pipelines.
They also, however, endanger companies with data overload, which occurs when there are no effective controls in place to handle the unforeseen rise in data. Such pipelines act as an appropriate channel for raw data and dataflows by filtering and refining the data such that top tier datasets that can be used for analysis and even decision making are generated.
Modern businesses today stand to reap invaluable rewards from all the different types of pipelines. These are the key tenets to grasp in order to understand the broader context of data pipelines.
Data Pipelines Builders – What Components Work Together
In data engineering, data pipelines are the backbone of how data flows from the source systems to the end users, ensuring automatic transformations and seamless analyses are performed concurrently.
Let’s consider the important components of the data pipeline:
1. Data Storage
Storage is a crucial part of the data pipeline since it allows the acquisition of raw and processed data at a low cost while preparing the pipeline for success. In other words, it provides a storage space that aids the execution of big data tools that too at a low cost.
This element ensures that the data is in place for downstream processes and analytics. For instance, storage may involve using data warehouses such as Snowflake, Amazon Redshift, & Google BigQuery, data lakes such as Azure Data Lake, Apache Hadoop & AWS S3 and operational databases to support real-time systems.
2. Preprocessing
The next component of the data pipeline is preprocessing, which is best described as “cleaning” or changing the original format of data to make it ready for the analysis.
The term refers, in this case, to cleaning up dirty inputs, eliminating duplicate entries, encoding unstructured data into formats that are machine readable, and standardizing structure formats. Also, it comprises of classification whereby relevant tags are assigned to parts of the data suitable for particular analysis. For example, tools such as Apache Spark, Python Pandas and AWS Glue are often used for preprocessing.
3. Analysis
One of the key components is the analysis of the data. This involves comparing newly acquired data against a reasonably large dataset. In addition, it allows businesses to pinpoint dependencies among elements data within large amounts of data and then form models that depict a given real life occurrence. This is accomplished simultaneously because advanced analytics techniques including, descriptive, predictive and prescriptive are employed at this level.
4. Applications
Applications are categorized as specific tools with required functionalities that allow processed data to transition into meaningful information. They encapsulate real life use case scenarios of the derived insights from the analyzed data. Applications guarantee that there is a clear alignment between the outcomes of the data pipelines and the objectives of the organization on which they are built.
5. Delivery
The last component of a data pipeline is delivery and it deals with the aspect of getting the processed configured data and all insights to the right locations as precisely and quickly as possible. Various reliable delivery methods like batch processing and real time streaming catered to the exact requirement of the target system are deployed. Still, delivery is comprehensive because it often includes monitoring and error-handling functions for data loss or latency to ensure continuous data availability for subsequent stages of the processes as well as decision making.
A solid data pipeline will bring all of these components together so that the data is transferred, processed and delivered as needed. This knowledge is essential in designing scalable and manageable data engineering systems.
Elevating Choices: Benefits of Data Pipelines for Businesses Abroad
A recent global survey cut across 1200 organizations and data pipelines came out as absolutely crucial and it showed that 74% of organizations who spent money on quality data pipelines enjoyed a profit surge of 17 percent on the average. The ability to make data – based decisions improves business efficiency associated with the use of availability of data while also minimizing manual workloads and likelihood of mistakes.
The top 5 benefits modern organizations have data pipelines for are as and ended with this one:
1. Greater Effectiveness
The repetitive tasks that data engineers spend doing repeatedly, transferring and changing data are automated through the use of data pipelines which allows for manual work and mistakes to significantly reduce while speed increases and frees engineers up to do more valuable tasks.
2. Data Leads To Insights In Real-Time
As earlier pointed out, one of the attributes of a modern data pipeline is the ability to process data in real time, this means that organizations who can afford to have this system are able to track their activities and make the relevant decisions when needed without having to reprocess the data which is very useful in the finance sector, e-commerce and health care industry.
For instance, as per an article, Mastercard employs smooth data pipelines for the purpose of processing millions of transactions in different parts of the world in real time. An automated data pipeline is also utilized by FedEx for the management of millions of packages all over the globe without manual updates being made. In a similar fashion, Uber also has robust data civic and process millions of rides and transactions every few minute and provide real time insights through their pipelines.
3. Scalability
Moving modern data pipelines has a limit, however, that limit is the ability to add more resources or to reduce them which makes it a multi fused tool. They are also prepared to manage large amounts of data and high levels of complexity. Without rebuilding the entire infrastructure, firms are able to expand their pipelines to include more data sources and execute more tasks.
As an example, Netflix’s Suro automatically increases its data streams for a set period, such as during the premiere of a new season of a well-known series or a movie that is eagerly awaited. In this manner, billions of people may view the content at the same time without lag or interference. More so, during the festive season and sales, Amazon too scales their data pipelines to accommodate the high volumes of orders.
4. Integration of Data Sources without any Interruption
This application integrates different kinds of data, assisting in dismantling the silos within an organization as it enables organizations to utilize disparate sources of information. This aggregated view of the data improves the quality of the insights that come from the analysis and the ability to work together better than previously. For example, Shopify utilizes sales data from thousands of online stores over the globe through the help of cloud-based platforms and their cloud native pipelines integration.
5. More Efficient Decision Tyme Making
Forrester’s report shows that less than one half of one percent of all data is ever analysed and used while only twelve percent of all the data generated by an enterprise finds its way into the decision-making process. Data pipelines play a crucial role in decision making, as the data that is sent helps in decision making since the data is reliable and accurate. There is enhanced strategic decision making, operational efficiency and in the end a strong positioning in the marketplace.
For instance, let’s consider the example of Spotify, which utilizes data pipelines to process over 4 billion user playlists, their listening history, their likes, website response times, etc. This information helps in providing suitable song recommendations, trend analysis, and improving the general experience of the users.
Although these companies are all well-known technological corporations, as we pointed out above, data pipelines tend to assist businesses in staying competitive no matter how big they are or what sector they operate in.
Managing a Data Pipeline – Encounters Pedestrians Recently Cover Generic Setbacks
Though, there is a couple of things to note, there is data to suggest that data pipelines are error-prone, require extensive debugging and are extremely labor-intensive to maintain and operate. To put things clearly, a survey conducted in 2021 pointed out that the biggest pain point for data engineers was the need to debug and maintain data pipelines.
This article seeks to explore the following strategies that could help overcome the steep barriers in data pipeline management:
1. Set up Concrete Monitoring and Alerting Features
In using tools like Grafana, Datadog or Prometheus for instance, companies can use such tools to inspect, track and monitor their data pipelines in real time. They assist quickly in diagnosing network congestion, lag and other abnormalities. Setting up alerts for premature failure, unusual degree of delay or other human events greatly increases the reliability of the pipeline. This also reduces the chances that unresolved problems erupt, ensuring that there are minimal outages and there is good throughput of the pipeline.
2. Improving Fault Tolerance And Resilience
By understanding that fault tolerance and resilience is at the core of a business’s requirements, there is a focus on creating pipelines that are dependable and possess the potential to be back on their feet post an event which caused disruption. Pipelining with built-in retry options and checkpointing ensures that there is minimal impact on the flow of data when some of the operational activities do not succeed. In the event of failure, the pipeline shifts to the last successful checkpoint and starts working from there thereby reducing idle time and saving loss of any data. Moreover, Way also ensures reliability and consistency through idempotent operations in which operations that are executed multiple times will not have unintended effects hence eliminating disruption of the entire data pipeline.
3. How To Enhance Scalability
When dealing with pipelines having massive amounts of data, it becomes exceedingly important to plan out strategies to efficiently and easily cope with the rising amount of data being handled therefore the key is always enhancing for scalability. Structural Scalability can be ideally achieved through the use of distributed frameworks like the Apache Spark or cloud-native solutions like AWS S3 and Google BigQuery For instance, These framework and solutions act as a gateway to efficiently process a large-scale dataset thereby allowing horizontal scaling of the `section of the data pipeline dealing with the load. Pipelines can be designed in a manner that the rising demand of data can be accounted for. This design focused on scalability revolutionizes the performance of the data systems.
This strategy guarantees that organizations will always optimize their performance and ensure that they are adapting to any data demands moving forward, even in the face of increased data volumes.
4. Maintain the Same Data Quality Throughout the Pipeline
‘Data is the new oil’, as the saying goes. With this much power in their hands, it is not enough for organizations to build their data pipelines at high quality, they also need to ensure that the insights built on these pipelines were also based on high quality data. One of the critical pillars is to automate data validation checks, which improve schema compliance and anomaly detection. When systems are designed to conduct these checks, businesses can then segregate the identified issues within the data prior to transmitting it throughout the streams. As a point of emphasis, this data answering the questions is often out of the pipeline and it is always a good practice to preprocess and clean that data more often. Data quality should have no other alternative and this data hygiene is done forever.
5. Make Use Of Workflow Orchestration Tools
Integrating workflow orchestration tools is an effective means to deal with issues such as task dependency, scalability, and error management. These tools ensure the smooth coordination of complex workflows by automating the processes of scheduling, monitoring and recovery, which are vital for preserving data integrity. They offer better control and visibility about the way a team’s pipeline operates, making it easier to resolve failures and maximize the use of resources.
Stride Digital combines innovative technology with in-depth industry knowledge to help businesses build real-time data pipelines which are geared towards enabling businesses to effectively control and analyze their data resulting in actionable insights that will elevate key business decisions and improve the operational efficiency of the organization. Our solutions ensure data accuracy, governance, and integration, allowing businesses to respond promptly to market disruptions. Stride Digital’s expertise of engineering the data, setting up governance frameworks, and real-time data processing allows businesses to build scalable and efficient workflows which are in line with their technological development goals. Be it advanced analytics, big data or master data management, we make sure that companies are well equipped to make sound data-based decisions that will aid in the acceleration of digital transformation goals.
Conclusion
According to statistics, in 2022, North America holds the largest share of the global data pipeline industry with 40.38%. The general industry which was worth $6.81 billion in 2022 is expected to experience tremendous growth to $33.87 billion by 2030, with a CAGR of 22.4%. Organizations benefit from using real time data pipelines because they allow the timely movement of data which helps them in decision making, market responsiveness and ensuring a positive customer experience. The market needs and trends indicate that as usage of data by companies increases, having a good real-time data pipeline is becoming critical in ensuring that companies have a competitive advantage and continue to grow and innovate.
Get in touch with us today and begin making plans on how to secure your business on achieving success in the future.
