A data pipeline helps your business to personalize the experience that each customer has with your brand, thereby helping to increase customer loyalty. It also helps to protect you from fraud, while improving your ability to identify the motivations of your clients. This article will explain what a data pipeline is and how it differs from an ETL pipeline.
What Is a Data Pipeline?
A data pipeline is a sequence of steps, designed to move raw data from its source to its destination. A source could be a transactional database, data scraped off the web, or even live measurements from sensors that you’ve placed around your factory.
A destination is wherever the data is being taken for analysis. For some data pipelines, the destination is called a sink. A destination could be a data lake or a data warehouse. However, it’s not always so. For example, a data pipeline could take data to an analytics database.
Transformation logic is often applied to the data while it’s moving from one point to another, so that by the time it reaches its destination, it’s ready to be analyzed. Nowadays, most companies use a variety of apps to manage marketing, sales, product development, and other aspects of their business. Data pipelines help them to gather data from all these sources, avoiding the effect of data silos.
Data silos limit business insights in several ways and can even make it difficult for you to identify your most profitable demographic. Fetching data from all the sources and copying it into a spreadsheet for analysis doesn’t work because you end up with issues like data redundancy. This kind of problem can skew your results.
For example, suppose you run an auto parts company. If you’re doing a query to find out how many tires of a particular size were sold on a specific date, you’ll want to count unique sales of those tires. Double counting errors would falsely inflate the figures.
When data from disparate sources is sent through the pipeline to one destination, you avoid errors like that. The data can also be analyzed quickly, so you’ll immediately know whether it makes sense to order more of those tires from your supplier or take any other action.
Data pipelines ensure that analysts always receive quality data to work with. Every investment that you make in business intelligence can positively impact your business. By ensuring that you have quality data, you can be confident that you understand what’s taking place in your stores and online.
Data Pipeline Architecture
There are several things that make up a data pipeline. These elements all work together to make it possible for pipelines to handle both structured and unstructured data.
Structured data consists of email addresses, phone numbers, and anything else that can be retrieved in a specific format. Unstructured data includes phone searches, social media comments, images, and a wide range of other data. It’s difficult to track unstructured data in a fixed format.
In order to prepare large data sets for any type of analysis, every data pipeline must have the following components:
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Source
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Destination
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Dataflow
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Processing
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Workflow
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Monitoring
Source
A source is any place that the pipeline extracts data from. This could be something like an IoT device, emails, or CRMs.
Destination
The destination is the final point on the pipeline and is often a data lake or data warehouse. At this endpoint, the data is stored until it’s required for analysis. Data from the pipeline can also have a different type of end point, such as a data visualization tool. These end points also facilitate prompt analysis.
Dataflow
Dataflow refers to the movement of data as it changes while going from the source to the destination. Extract, Transform, Load, or ETL, is one of the most common approaches to dataflow.
Processing
Processing describes all the steps from extraction to transformation and, finally, the delivery of data to its destination. The processing component determines how dataflow should be done.
For example, different extraction processes can be used for ingesting data and the processing component decides which of those processes is best. Stream processing and batch processing are the two most common methods of extraction.
Workflow
Workflow describes the way in which jobs are sequenced in the pipeline and how they are related to each other. These factors also determine when a pipeline runs. In typical situations, upstream jobs are done before those further downstream.
Monitoring
A data pipeline consistently delivers quality data because it’s always being monitored. Through monitoring, any type of data loss or inaccuracy will be detected and addressed quickly.
Data pipelines aren’t very useful if they can’t get data to the destination quickly, so they’re also monitored for efficiency and speed. Throughput, latency, and reliability all contribute to the speed at which data moves through the pipeline. All of these factors are affected as the volume of data gets bigger, so constant monitoring is critical.
Data Pipeline Tools
A data pipeline tool automates the process of moving data from its source to its destination. Data pipeline tools can handle the type of data sources that you have now and those that you might include in your analysis in the future.
Data reliability is a critical feature of these tools. They can transform and load data without errors. Some provide data in real time while others transfer data in batches. You’ll have to ensure that the tool you pick transfers data at a pace that matches your needs.
Tools can be divided into several different categories, according to their purpose. For example, some tools are batch data pipeline tools, while others are real-time tools. Batch tools let you transfer a lot of data in real time, so if you have large volumes of data coming from many different sources, you can move them to the destination on a schedule that meets your needs.
Several companies need to have information at their fingertips because they’re always testing an aspect of their business processes. They need to know right away if these changes are having a negative or positive effect on sales or the perception of their brand. For this reason, a real-time data pipeline would be their ideal choice.
Many businesses nowadays have data coming from a streaming source. For example, they might have mobile apps and they want to use the data that’s generated from user interactions. Real-time data pipeline tools are a good fit for this type of situation.
Real-time tools are also ideal for companies that want additional protection from fraud. If your business handles lots of credit card transactions, you can continuously ingest this type of data and set up fraud alerts within your system.
Data pipeline tools can also be classified as open-source or proprietary. Open-source tools make the underlying technology publicly available. They are usually free or have a nominal fee attached and this is one of their major advantages. However, you’ll need to understand how to set them up on your own.
Proprietary data pipeline tools are developed for specific business purposes, so you don’t need to customize any aspect. Since you can start using them immediately, you’ll save time and can gain insights fast, which help you to improve your business. You also won’t need a lot of expertise to profit from this type of software.
You can also find data pipeline tools divided into separate categories, based on whether they’re on-premises or cloud-native applications. In the past, many businesses established their data lakes and data pipelines on their premises. Doing so gives a business a significant measure of security, since everything is deployed on their own infrastructure.
Cloud-based tools are also secure. The difference is that this type of software works with cloud-based data and data warehouses that are hosted on the cloud. Companies that use this option save money on infrastructure because the data pipeline is hosted by the vendor.
Data Pipeline Examples
There are several data pipeline tools available and all of them can help you to automatically pull data from a range of sources that are important to your business. The tool that you use should be intuitive, allowing you to build a pipeline even without extensive training in the software. No one on your team has the time to spend weeks learning how to use a new platform.
Intuitive tools help you to build your infrastructure without delays that cost money and leave your managers unaware of what is happening in your business. The best tools start working out of the box and require very little in the way of maintenance. You can expect to have a smooth flow of data, without unplanned interruptions.
These are a few examples of the best data pipeline tools that are available:
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Mozart
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Blendo
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Fly Data
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Apache Airflow
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Talend
All of these tools have their advantages. For example, Mozart is ideal for companies that may want to write their own transforms but don’t want to handle other aspects. Mozart offers businesses an out-of-the-box solution that is branded as a modern data stack. Companies can get everything in one place to meet their needs, including data pipelines.
Talend is an example of. a batch data pipeline tool. If your business has limited resources and it’s not ideal for you to be constantly transferring data in real time, batch tools are best.
What Is An ETL Pipeline?
An ETL pipeline is a set of processes that extract data from a source, transform it, and then load it to a destination. Data can be extracted from sources such as APIs and transaction databases. Then it can be loaded to destinations like Amazon RedShift, a data warehouse, or a database.
Some companies use ETL pipelines to enhance their CRM systems with extra data. Others might use ETL pipelines to transform data internally among several data stores.
Data Pipelines vs ETL Pipelines
Data pipelines and ETL pipelines differ in three main ways. Firstly, ETL pipelines are a subset of data pipelines. ETL pipelines are a particular type of data pipeline that always ends with loading.
However, all data pipelines do not end with loading. While loading occurs in all data pipelines, in some, it causes another step to take place, so it’s not the final step.
Secondly, all data pipelines don’t involve transformation. An ETL pipeline is a particular type of pipeline that uses transformation.
ETL pipelines also run in a way which is different from some other data pipelines. ETL pipelines are run in batches but several other data pipelines run in real time. This makes these pipelines ideal for some applications.
While ETL pipelines run in batches, steps can be taken to ensure that the data is current. Each chunk of data is moved on a regular schedule, so this could be twice a day or at a specific time when system traffic would usually be low.
How Mozart Helps With Data Pipelines
Mozart manages data warehouses on Snowflake for their customers. They also automate the ETL pipelines that go into and leave those warehouses.
Mozart provides an intuitive interface that allows you to schedule data transformations. Anyone on your team can use Mozart’s tools to build and easily visualize data transformations. You can connect any data visualization tool that you prefer to use.
Mozart allows you to connect to numerous types of data sources. Most teams can start querying data from their SaaS database in less than an hour after they’ve procured Mozart.
Unlike most data companies that focus on a single aspect of the data pipeline, such as warehouses or ETL, Mozart offers a one-stop shop. Companies like Zeplin and Rippling currently use Mozart to automate dashboards that provide information on their key metrics.
Mozart empowers companies with sales teams that have a lot of data to work with but don’t have the bandwidth to maximize it. You can load, query, and clean your data in Mozart. You can write your own SQL transforms and they’ll take care of scheduling and other aspects for you, freeing your team to work on other tasks.
Mozart Data can build dashboards and reports tailored to your company’s objectives and aspirations using all of your linked sources of data. These dashboards will help track results in real-time throughout your tools, including email and ad campaign performance, CAC and LTV for all of your acquisition channels, and conversion funnels.
You will be able to use your data in this manner to make successful and timely business decisions that will help you build your business. You’ll be able to quickly integrate new data sources into your Mozart account as you add more tools to operate your business.
You must do research that incorporates important data from all of your instruments. For many businesses, this entails merging data from marketing and advertising, website/app analytics, social media, point of sale, and customer service platforms. Making a great system that integrates all of your data, on the other hand, can be unpleasant, time-consuming, and expensive.
Mozart Data saves you time by automatically pulling information from all of your company’s platforms. It will also save you the time and effort of determining where to keep your data so that a uniform analysis can be performed throughout your entire organization. Too often, the results of one team are drastically different from those of another simply because their data source is inconsistent.
You can only obtain a deeper insight into your clients and the true success of your acquisition channels by merging data sources.
