If you're a 21st-century business, you've likely considered hiring a data scientist. If you haven't, blame the newness of the field: data science only entered the business lexicon in 2001. That's when William S. Cleveland introduced it as an extension of the field of statistics. Then, in 2009, Hal Varian (Google's chief economist) offered a prescient insight. He proposed that the process of harvesting massive amounts of data and extracting value from it would transform modern business.

He was right. Today, data scientists create machine learning algorithms to solve intricate business challenges. These algorithms help:

  • Improve predictive fraud capabilities
  • Identify the motivations and inclinations of consumers down to a granular level. By extension, this helps promote brand awareness, reduce financial burdens, and increase revenue margins.
  • Predict future customer demand for optimum inventory deployment
  • Personalize the customer experience

In order to achieve those outcomes, data pipelines are a crucial piece of the puzzle. Here's why data pipelines are important, their benefits, and how to go about building your own data pipeline.

Table of Contents:

  1. What is a Data Pipeline?
  2. Why You Need a Data Pipeline
  3. The Two Main Types of Data Passing Through a Data Pipeline
  4. The Elements of a Data Pipeline
  5. The Differences Between a Data Lake and a Data Warehouse
  6. How Do You Create a Data Pipeline?
  7. Using Xplenty to Build Data Pipelines

What is a Data Pipeline?

Simply speaking, a data pipeline is a series of steps that move raw data from a source to a destination. In the context of business intelligence, a source could be a transactional database, while the destination is, typically, a data lake or a data warehouse. The destination is where the data is analyzed for business insights.

In this journey from the source to the destination, transformation logic is applied to data to make it ready for analysis.

Why You Need a Data Pipeline

The proliferation of the cloud has meant that a modern enterprise uses a suite of apps to serve different functions. The marketing team might employ a combination of HubSpot and Marketo for marketing automation, the sales team might rely on Salesforce to manage leads, while the product team might use MongoDB for storing customer insights. This leads to the fragmentation of data across different tools and results in data silos.

Data silos can make it difficult to fetch even simple business insights, such as your most profitable market. Even if you manage to fetch data from all the different sources manually and consolidate it into an Excel sheet for analysis, you can run into errors such as data redundancy. Moreover, the effort required in doing it manually is inversely proportional to the complexity of your IT infrastructure. Throw-in data from real-time sources, such as streaming data, and the problem becomes exponentially more complex.

Data pipelines, by consolidating data from all your disparate sources into one common destination, enable quick data analysis for business insights. They also ensure consistent data quality, which is absolutely crucial for reliable business insights.

The Two Main Types Of Data Passing Through A Data Pipeline

Broadly speaking, there are two types of data that pass through a data pipeline:

  • Structured Data: This type of data can be saved and retrieved in a fixed format. This includes device-specific statistics, email addresses, locations, phone numbers, banking info, and IP addresses.
  • Unstructured Data: This type of data is difficult to track in a fixed format. Email content, social media comments, mobile phone searches, images, and online reviews are some examples of unstructured data.

In order to extract business insights from data, you need dedicated infrastructure for data pipelines to migrate data efficiently.

The Elements of a Data Pipeline

To understand how a data pipeline prepares large datasets for analysis, let's look at the main components of a typical data pipeline. These are:

1) Source

These are places where a pipeline extracts data from. They can include relational database management systems (RDBMS), CRMs, ERPs, social media management tools, and even IoT device sensors.

2) Destination

This is the endpoint for the data pipeline, where it dumps all the data it has extracted. Very often, the destination for a data pipeline is a data lake or a data warehouse, where it is stored for analysis. However, that's not always the case. For example, data can also be fed directly into data visualization tools for analysis.

3) Dataflow

Data undergoes changes as it travels from source to destination. This movement of data is called dataflow. One of the most common dataflow approaches is ETL, or extract, transform, load.

Related Reading: What is ETL?

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4) Processing

These are the steps involved in extracting data from sources, transforming it, and moving it to a destination. The processing component of a data pipeline decides how dataflow should be implemented. For example, what extraction process should be used for ingesting data. Two common methods of extracting data from sources are batch processing and stream processing.

Related Reading: 5 Types of Data Processing 

5) Workflow

Workflow is concerned with the sequencing of jobs in a data pipeline and their dependence on each other. Dependencies and sequencing decide when a data pipeline runs. Typically, in data pipelines, upstream jobs should be successfully completed before downstream jobs can begin.

6) Monitoring

A data pipeline needs consistent monitoring to check for data accuracy and data loss. A pipeline is also monitored for speed and efficiency, particularly as the size of data grows larger.

The Differences Between A Data Lake and Data Warehouse

While data lakes contain raw data, data warehouses store standardized data. Basically, data warehouses contain processed data that are ready to be analyzed. Business professionals can extract value from standardized data because it corresponds directly to the existing ways they manage data in inter-office spreadsheets.

On the other hand, data lakes contain unfiltered, raw data that's unorganized. This data is less accessible to employees. However, it holds great potential, in that data scientists can use machine learning analytics to make market-driven recommendations. Essentially, data lakes allow data scientists to deliver deep insights into complex problems. This, in turn, allows company stakeholders to make more informed decisions.

Related Reading: 7 Critical Differences Between a Data Lake and a Data Warehouse

When You Should Use A Data Lake

  • When data needs to be collected for business monitoring and operational reporting
  • When you need to combine and manage supply chain file-based data
  • When the type of analytical queries aren't known in advance
  • When the data needs to be made accessible to more stakeholders in an organization

When You Should Use A Data Warehouse

  • When you need data that's pre-aggregated and ready to use
  • When records and data items must be represented by a schema framework, such as the star or snowflake schema
  • When data queries are known in advance
  • When exclusive access to specific data must be protected
  • When you need to have 100% trust in the reliability of your data

How Do You Create a Data Pipeline?

To build a data pipeline, an enterprise has to decide on the method of ingestion it wants to use to extract data from sources and move it to the destination. Batch processing and streaming are two common methods of ingestion. Then there is the decision of what transformation process - ELT or ETL - to use before the data is moved to the required destination. We explain the basic difference between the two processes later in this article.

And that's just the starting point of creating a data pipeline. There is a lot more that goes into building low-latency data pipelines that are reliable and flexible.

Do You Really Need A Data Scientist to Build Data Pipelines?

Little consensus exists regarding this. At present, data scientists are in hot demand, but no one quite knows what qualifications they should have.

To fill the void, the Open Group (an IT industry consortium) announced three levels of certifications for data scientists in early 2019. To earn the certifications, candidates need to demonstrate knowledge of programming languages, big data infrastructures, machine learning, and AI.

Not too long ago, data scientists were needed to build data pipelines. Today, solutions like Xplenty allow you to build your own data pipelines without really knowing how to code.

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Should You Build Your Own Data Pipeline?

Some big companies, such as Netflix, have built their own data pipelines. However, building your own data pipeline is very resource and time-intensive. Moreover, there is ongoing maintenance involved, which adds to the cost.

Here are some common challenges of building a data pipeline in-house:

1) Connections

As a modern enterprise, you are likely to add new data sources as you go along. Every time you add a new data source, it needs to be integrated into the pipeline. And integrations can run into issues, either due to lack of proper API documentation, or differing protocols. For example, a company might use SOAP APIs instead of REST APIs.

APIs can also change or break, which means they need consistent monitoring. As the complexity of your data sources grows, you will need to allocate more time and resources, simply for the maintenance of APIs.

2) Latency

The faster a data pipeline is able to transfer data to a destination, the fresher is your business intelligence. However, extracting data in real-time from several different sources is easier said than done. There is also the issue of some databases not optimized for real-time processing, such as Amazon Redshift.

3) Flexibility

Your data pipeline should be able to handle changes quickly. These changes can be in the form of different types of data or fluctuations in APIs. For example, changes in an API might result in unexpected characters that the pipeline hasn't handled in the past. You need to build for such scenarios to prevent your data pipeline from breaking.

4) Centralization

Typically, with in-house data pipelines, there is a central It team with programmers in charge of building and maintaining pipelines. That raises two key concerns: the cost of hiring a dedicated engineering team can be exorbitant. Second, and more importantly, it leads to the centralization of data processing, which is not very efficient. With cloud data pipelines, such as Xplenty's low-code solution, each business team can build their own pipelines within minutes and start gathering business insights. The decentralization of data processing can be a huge advantage for achieving operational efficiency.

Some other challenges in building your own data pipeline include:

  • Accuracy of Data: Poorly built pipelines can result in situations where the source and target schemas don't match. Incompatible schemas can negatively affect the efficacy of your pipeline.
  • Scalability: Scenarios where the data pipeline infrastructure can't support increasing data volumes and the evolution of data sources.

Bottom line: while it is certainly possible to build your own data pipeline, it is not the ideal solution. Instead, cloud data pipelines are a much better option. They are highly scalable, with on-demand computational power, close to zero downtime, and robust data security.

Using Xplenty to Build Data Pipelines

Xplenty is a visual, point-and-click platform that enterprises can use to build data pipelines within minutes. The data integration platform comes with out-of-the-box data transformations that can save your engineering team a lot of valuable time. The low-code platform can handle simple replication tasks as well as complex data transformation use cases.

It is compatible with the most popular data stores and SaaS platforms. And you can integrate almost any data source in your data pipelines with the help of REST APIs on Xplenty. Compared to building and maintaining your own data pipelines, using Xplenty for the job keeps your costs down while still keeping your enterprise scalable at will.

Most importantly, though, Xplenty lets you build and deploy ETL pipelines, which are more robust and cost-effective, compared to ELT pipelines.

ELT Versus ETL

The two are data transformation techniques, where E stands for Extract, L for Load, and T for transformations. Broadly speaking, in ETL, data moves to a staging area after extraction. Transformations are applied to the data before it is loaded into a data warehouse.

In ELT, on the other hand, data warehouses are used for basic data transformations. There is no staging area involved. The biggest advantage of ETL is it is better at handling sensitive data. Critical data can be cleaned before data is moved to a warehouse for analysis. Thus, businesses can meet data compliance laws more effectively, saving themselves from hefty fines and loss of customer trust. ETL is also able to handle more complex data transformations compared to ELT.

Related Reading: 5 Differences Between ETL and ELT

Big Data Architecture

Lambda and Kappa architectures are two of the most popular big data architectures. Lambda architecture comprises a Batch Layer, Speed/Stream Layer, and Serving Layer. It is, in a nutshell, a system of dividing data systems into "streaming" and "batch" components.

Essentially, Lambda is a hybrid system that can process both OLAP (analytical) and OLTP (transactional) applications.

Gartner refers to Lambda as an HTAP (Hybrid Transaction/ Analytical Processing) system. HTAP consists of Point-of-Decision HTAP and In-Process HTAP.

Point-of-Decision HTAP is a data architecture that deploys in-memory computing (IMC) to enable simultaneous analytic and transaction processing. Meanwhile, In-Process HTAP combines both analytic and transaction processing to deliver real-time, hyper-personalized user experience (UX).

Kappa architecture, on the other hand, is a version of Lambda with batch processing disabled. Both Lambda and Kappa have their advantages. However, their collective disadvantages may prove daunting to business professionals who want to build their own data pipelines.

For Lambda, the operational challenge of debugging two disparate systems may prove insurmountable. Additionally, the problem of data reprocessing can't be ignored. Meanwhile, Kappa isn't a one-size-fits-all solution, despite its streaming capabilities.

If you're looking to build an efficient, low-latency pipeline, Xplenty's intuitive graphic interface lets you get up and running in minutes. Our pre-built integrations let you connect to almost all the popular data tools out there, including databases, BI tools, and analytic tools. Schedule your demo and see for yourself how easy it is to design and run your data pipelines on Xplenty.