Big Data Pipelines can be described as subsets of ETL solutions. Like typical ETL solutions, they can dabble with semi-structured, structured, and unstructured data. The flexibility allows you to extract data from technically any source. Big Data Pipelines can also leverage the same transformations and load data into various repositories, including Data Lakes, relational databases, and Data Warehouses.
This article delves into the various salient aspects of a Big Data Analysis Pipeline: its key features, examples of architecture, best practices, and use cases. It also briefly introduces a Data Pipeline before diving into the nitty-gritty of Big Data Pipelines.
Table of Contents
What is a Data Pipeline?
A Data Pipeline can be described as a sequence of data processing steps. If the data is not currently loaded into the platform, it must be ingested at the beginning of the pipeline. This is followed by a series of steps, each providing an output that serves as the input for the next step. This continues until the pipeline is complete. In a couple of cases, you can run independent steps concurrently.
Data Pipelines contain three key elements: a source, a processing step or a set of steps, and a destination (also known as a sink). These pipelines enable data flow from an application to a Data Warehouse, for instance, from a Data Lake into a payment processing system or an analytics database. Data Pipelines can also have the same source and sink, so the pipeline is purely about changing the data set. Any time the data is processed between points A and B (or points C, B, and D), a Data Pipeline bridges those points.
As organizations look to build applications with small code bases that serve a specific purpose, they are moving data between more and more applications, making the efficiency of Data Pipelines a critical consideration in their development and planning.
Data Pipeline architectures can explain how they’re set up to enable data collation, delivery, and flow. Data can either be moved through stream processing or batch processing. In Batch Processing, data clusters are migrated from the source to the sink on a regularly scheduled or a one-time basis. On the other hand, Stream Processing allows for real-time data movement. It continuously collects data from sources like events from sensors and messaging systems or change streams from a database.
What is a Big Data Pipeline?
As the variety, volume, and speed of data have considerably grown in recent years, developers and architects have had to adapt to Big Data. Simply put, Big Data means that there is a huge volume to deal with. This massive volume of data can open opportunities for use cases such as real-time reporting, alerting, and predictive analytics, among other examples.
The most poignant difference between regular Data Pipelines and Big Data Pipelines is the flexibility to transform vast amounts of data. A Big Data Pipeline can process data in streams, batches, or other methods, with their set of pros and cons. Irrespective of the method, a Data Pipeline needs to be able to scale based on the organization’s needs to serve as an effective Big Data Pipeline. Without scalability, the system might take weeks or days to complete its job.
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Key Features of a Big Data Pipeline
Here are a few key features that allow a Big Data Pipeline to stand out:
Scalable Cloud-Based Architecture
Big Data Pipelines depend on the Cloud to allow users to automatically scale storage and compute resources down or up. While traditional pipelines aren’t designed to handle multiple workloads concurrently, Big Data Pipelines house an architecture in which compute resources are distributed across independent clusters. Clusters can grow in size and number quickly and infinitely while maintaining access to the shared dataset. It is easier to predict the data processing time as new resources can be added instantly to support spikes in data volume.
Cloud-based Data Pipelines are elastic and agile. They let businesses take advantage of various trends as well. For instance, a company that expects a summer sales spike can easily add more processing power when required and doesn’t have to plan weeks for this scenario. In the absence of elastic Data Pipelines, businesses can find it difficult to quickly adapt to trends.
Data Pipeline failure is a real possibility while the data is in motion. To mitigate the impacts on mission-critical processes, today’s Data Pipelines provide a high degree of availability and reliability.
Big Data Pipelines are designed with a distributed architecture that alerts users and provides immediate failover in the event of application failure, node failure, and failure of certain other services.
If a node goes down, another node within then the cluster can immediately take over without needing major interventions.
Transforming High Volumes of Data
Since semi-structured and unstructured data make up around 80% of the data collated by companies, Big Data pipelines should be equipped to process large volumes of unstructured data (including sensor data, log files, and weather data, to name a few) and semi-structured data (like HTML, JSON, and XML files). Big Data Pipelines might have to migrate and unify data from sensors, apps, log files, or databases. Often data has to be standardized, enriched, filtered, aggregated and cleaned – all in near real-time.
Real-Time Analytics and Data Processing
Big Data Pipelines should transform, ingest, and analyze data in near real-time so that businesses can quickly find and act on insights. To start with, data needs to be ingested without delay from sources including IoT devices, databases, messaging systems, and log files. For databases, log-based Change Data Capture (CDC) serves as the gold standard for producing a stream of real-time data. Real-time Data Pipelines provide decision-makers with the latest data at their disposal.
Big Data Pipelines are constructed using tools that are linked to each other. From Data Warehouses and Data Integration platforms to Data Lakes and programming languages, teams can leverage various tools to easily maintain and develop Data Pipelines in a self-service and automated manner.
Traditional Data Pipelines usually need a lot of effort and time to integrate a vast set of external tools for data transfer, data extraction, and analysis. Ongoing maintenance can be time-consuming and causes bottlenecks that introduce new complexities.
Big Data Pipelines also democratize access to data. Tackling all types of data is more automated and easier than before, allowing businesses to take advantage of data with less in-house personnel and effort.
Streamlined Data Pipeline Development
Big Data Pipelines are developed following the principles of DataOps, a methodology that brings various processes and technologies to shorten development and delivery cycles. Since DataOps deals with automating Data Pipelines across their entire lifecycle, pipelines can deliver data on time to the right stakeholder.
By aligning pipeline deployment and development, you make it easier to scale or change pipelines to include new data sources.
Data duplication and data loss are a couple of common issues faced by Data Pipelines. Big Data Pipelines offer advanced checkpointing abilities that ensure no events are processed or missed twice. Checkpointing tracks the events processed and how far they go down different Data Pipelines.
Checkpointing meshes with the data replay feature that’s provided by various sources, letting you rewind to the correct spot if a failure occurs. For sources without a data replay feature, Data Pipelines with persistent messaging can checkpoint and replay data to ensure that it has been processed only once.
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Importance of a Big Data Pipeline
Like various components of data architecture, Data Pipelines have also evolved to back Big Data. Big Data Pipelines are Data Pipelines that are built to accommodate one or more of the three key traits of Big Data.
The speed of Big Data makes it engaging to construct streaming Data Pipelines for Big Data. This allows data to be extracted and transformed in real-time to accomplish an action.
The volume of Big Data needs Data Pipelines to be scalable since the volume can vary over time. Realistically, there can be many Big Data events that occur concurrently or very close together, so the Big Data Pipeline should be able to scale to transform considerable volumes of data simultaneously.
The variety of Big Data needs Big Data Pipelines to have the ability to transform and identify data in many different formats – unstructured, structured, and semi-structured.
Components of a Big Data Pipeline
To run a Big Data Pipeline seamlessly here are the three components you’ll need:
The compute component allows your data to get processed. Some common examples of Big Data Compute frameworks are as follows:
- Apache Flink
- Apache Spark
- Hadoop MapReduce
- Apache Heron
- Apache Storm
These compute frameworks are responsible for running the algorithms along with the majority of your code. For Big Data frameworks, compute components handle running the code in a distributed fashion, resource allocation, and persisting the results.
There are all different levels of sophistication on the compute side of a Data Pipeline. From the code standpoint, this is where you’ll be spending the majority of your time. This can also take the blame for the misconceptions around compute being the only technology that is required.
Messaging components are used to migrate events or knowledge from point A to point B in real time. Some commonly used instances of messaging platforms are as follows:
- Apache Pulsar
- Apache Kafka
- RabbitMQ (doesn’t scale)
It is recommended to leverage messaging components when there is a need for real-time systems. These messaging frameworks can be used to extract and propagate a large amount of data. This propagation and extraction are pivotal to real-time systems because it solves mobility issues. From a coding and architecture perspective, you will be spending an equal amount of time on both. This will require you to gain a better understanding of user access patterns and use cases where the code can be leveraged.
Some technologies can be a mix of two or more components. However, there are important nuances that you need to be aware of. For instance, Apache Pulsar is primarily a messaging component but can also be used for storage and compute needs.
Storage components are responsible for the permanent persistence of your data. A couple of examples of simple storage are as follows:
- Cloud filesystems like Amazon S3
- Local Storage (doesn’t scale)
For simple storage needs, people would just dump their files into a directory. As it becomes slightly more difficult, you can begin using partitioning. This will put files in directories with particular names. A commonly used partitioning method is to use the date of the data as part of the directory name.
Types of Big Data Pipelines
Here are the different types of commonly used Big Data Pipelines in the marketplace:
ETL is the most common Data Pipeline architecture, one that has been the standard for several decades. It takes raw data from various sources, transforms it into a single pre-defined format, and loads it to the sink – typically a Data Mart or an enterprise Data Warehouse.
Typical use cases for ETL Pipelines include the following:
- Collecting high volumes of data from different types of external and internal sources to offer a holistic view of business operations
- Extracting user data from several touchpoints to have all the information on customers in a single place (usually in the CRM system)
- Linking disparate datasets to allow deeper analytics.
The primary downside of the ETL architecture is that you need to rebuild your data pipeline every time business rules are modified. To address the problem, another approach gained prominence over the years — ELT.
ELT differs from ETL in the sequence of steps; loading takes place before transformation here. Due to this small change, instead of modifying large amounts of raw data, you first move it directly to a Data Lake or Data Warehouse. This will allow you to structure and process your data as needed — at any moment, partially or fully, numerous times or just once.
ELT architecture would come in handy for the following use cases:
- Scenarios where the speed of Data Ingestion plays a key role.
- Situations where you aren’t sure what you’re going to do with the data and how would you go about transforming it.
- ELT architecture also comes in handy where large volumes of data are involved.
Your decision to transition to Operational Analytics using reverse ETL can become a turning point for your business. It is one step forward to become more data-driven and adapt your products and services to better suit your customers.
Operational Analytics can be facilitated if you either build or buy Reverse ETL. It is essential for your data management stack to make effective and factual decisions. This ensures efficient use of workflows, operational decisions, and systems that drive benefits to your business.
A dynamic trend as such isn’t something new. As the volume of business data and time constraints grow, executive decision-making is being pushed down to operational teams. It is now more vital than ever to provide your employees and teams with accurate data to make operational decisions swiftly and implement them to reap the rewards.
Here are a few use cases of Reverse ETL:
- If the marketing team needs direct access to the frequency of client purchases within its marketing automation solution to create more powerful scenarios, reverse ETL can come in handy.
- Reverse ETL can also be leveraged by the customer success team if they wish to send tailored emails based on the usage of a product.
Building an indigenous reverse ETL solution can help you better tailor your solution to your business use case and needs, but customizing your reverse ETL solution can get unwieldy and requires a ton of assessment of your current business operations.
You may be hesitant, but spending extravagant amounts of money and labor on creating a reverse ETL solution would be heedless to know that similar functionality is offered if you buy reverse ETL.
You can buy Reverse ETL tools such as Hevo Activate, a fantastic- simpler & speedier solution that runs in the cloud. Hevo Activate can operationalize your business data from data warehouses like Snowflake, Amazon Redshift, Google BigQuery, and Firebolt to target destinations like CRM, Support Apps, Project Management Apps, and Marketing Apps with no difficulty.
Key Big Data Pipeline Architecture Examples
Organizations typically depend on three types of Data Pipeline transfers:
Streaming Data Pipeline
Real-time streaming dabbles with data moving onto further processing and storage from the moment it’s generated, for instance, a live data feed. The stream processing engine can provide outputs from the Data Pipeline to data stores, CRMs, and marketing applications. From an implementation point of view, streaming data processing can leverage micro-batches that can be completed within short time windows.
Batch Data Pipeline
In batch processing, you simply collect data fragments in temporary storage and send them as a group on a schedule. You can execute this when access to this data isn’t urgent, or there are intermittent latency issues to deal with.
Lambda architecture tries to combine real-time and batch streaming by having them sync to storing data in the same file by regularly adding to it. This can be a little complicated to accomplish in-house since the real-time and batch components are independently coded and need to be in sync with the file writing.
However, AWS Lambda functions have a couple of limitations that you should be aware of. For instance, the Lambda timeout is 15 minutes, and the memory size limit is 10 GB. This method is recommended for short-term tasks that need a lot of memory per task. One key aspect of this architecture is that it encourages storing data in a raw format so that you can continuously run new Data Pipelines to rectify any code errors in prior pipelines or generate new data destinations that allow new types of queries.
Key Use Cases of Big Data Pipelines
There are a couple of industries that depend on Big Data more than others. These include:
- Construction companies track everything from the hours put into material costs.
- Brick-and-mortar and online retail stores that track consumer trends.
- Finance and banking institutions use Big Data to predict data trends and improve customer services.
- Healthcare organizations analyze voluminous data to find effective treatments.
- Organizations that dabble in media, entertainment, and communications leverage Big Data in various ways, such as offering real-time social media updates, improving connections between smartphone users, and improving HD media streaming.
- Colleges, schools, and universities measure student demographics, improve the student success rate, predict enrollment trends, and ascertain which educators excel.
- Energy companies leverage Big Data Pipelines to identify problems quickly to start finding solutions, handle workers during crises, and provide consumers with information that can help them use less energy.
- The government can leverage these pipelines in several ways, such as analyzing data to process disability claims, detect fraud, identify illnesses before they impact thousands of people, and track changes in the environment.
- Natural resource and manufacturing groups require Big Data Pipelines to align their activities to deliver the products that consumers need, lower overheads, and recognize potential dangers.
Best Practices to Build Big Data Pipelines
Here are a couple of best practices to keep in mind when building a Big Data Pipeline:
- They should be scalable so that they can be sized down or up based on the demands of system warrants. Sizing down is often neglected but is important for managing cloud hosts.
- These Data Pipelines should be highly extensible since this would allow them to incorporate as many things as possible.
- They should also be idempotent so that they can generate the same result no matter how many times the same initial data is used. Logging should take place at the completion and inception of every step.
- You should start simple, for instance, with serverless, with as few pieces as needed. You can then move to a full-blown pipeline or your deployment, only when the Return on Investment (ROI) is justifiable. A piece of helpful advice is to bootstrap with minimal investment in the computational stage. You can even start a “compute-free” pipeline by executing computations by scheduling various cloud functions and SQL queries. This would get the whole pipeline ready faster, giving you ample time to handle your data strategy, along with data catalogs and data schemas.
This article talks about the salient aspects of Big Data Pipelines in great detail namely its features, use cases, best practices, and components of Big Data architecture to name a few.
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