What is a data pipeline?

Posted By: administrator
Posted On: 24 Nov 2019

Before we start talking about the data pipeline, let us look at why the industry surrounding the data pipeline has been growing significantly. Over the last 15 years, there has been significant growth in the adoption of software-as-a-service applications. Used to be a monolithic application supporting various business functions like Finance, CRM, inventory, Asset Management, customer support, and manufacturing has been decomposed into the best of the breed software-as-a-service applications. A company was using monolithic applications may now be using multiple SaaS applications to do the same functions.

At the same time, a rise in social media and an increase in the adoption of Internet Technologies introduced a new class of applications specifically around advertising and marketing, which have also found much adoption in the industry. In an e-commerce company as an example, it is not uncommon to find them using upwards of twenty different applications to operate their business. In addition to this, application usage at the company also tends to change more frequently than they used to before. A big driver of this phenomenon is the fact that signing up for a SaaS application typically requires just a swipe of a credit card and a willingness to try the service and no need to talk to a salesperson. As an example, if you don’t like a customer service software A, then the cost of trying out and switching to a customer service software B, has reduced significantly. With the rise of rest APIs, migrating applications from one system to the one had become more common than it used to be when applications were more closed.

You might be wondering what bearing does a rise in SaaS applications and REST APIs have to do with a data pipeline? 

In the past, when companies embarked on their journey to build a data warehouse, they may have had to support a handful of applications that were relatively stable. Applications have now multiplied and created an increase in the demands on the IT and data engineering teams to keep up with the dynamic nature of the application landscape in the companies. As a result, the traditional way of doing data warehousing is no longer able to support modern business requirements.  

If you are new to data warehousing, please read our article on data warehouses.

One of the most critical operations in a data-driven company is the flow of data from SaaS applications, files, databases, and marketing applications to a data warehouse. Business leaders rely on reporting and analyses to make informed decisions. What use will data be to them if it is not available to these resources when they needed it the most? Moving data from various types of data sources is generally more complicated than it may seem. There could be issues with data loss, data complexity, duplicate data, latency, and throughput. The complexity of moving data also increases significantly with the volume, velocity, and the variety of data from the data sources.

The data pipeline for modern teams

A data pipeline is a piece of cloud-based SaaS software that simplifies the flow of data from various sources to a destination. A data pipeline simplifies this flow by allowing users to use a simple, easy-to-use user interface to define the parameters of the flow, the configuration of source, and that of the destination. The complexity of the data flow management is handled entirely by the data pipeline allowing the user to set and forget a data flow configuration while fully expecting to move the data to the desired destination. It also provides visibility into what a data pipeline is doing so that the user can now comfortably offload the burden of management to the pipeline and focus on developing utilities like reports, dashboards, and analyses to impact the business positively. A data pipeline has become one of the core applications for a data-driven company.

A data pipeline provider performs the same function as that of a water utility company. You pay the bill, and you expect clean, healthy drinking water when you turn on the tap.

12 signs that indicate you need to invest in a data pipeline

Below are some telltale signs that it is time to invest in a data pipeline.

No data warehousing

In an age where businesses are competing ever so aggressively to gain new customers, retain existing customers, and improve operational efficiency, not having a data warehouse pushes companies ever so close to losing competitiveness. With the advent of cloud computing and the rise of cloud-native, serverless data warehouses, it is about time that companies start to take data warehousing seriously. Read here for a more in-depth view of why we feel so.

Aging Data Warehousing infrastructure

Data warehousing has traditionally been an IT function, carefully curated, and managed. It was necessary to ensure the fulfillment of the demands of critical resources before addressing less pressing needs. It was also done to ensure governance. But how is this infrastructure supporting the demands of growing data science and analytics teams whose task is to find avenues to improve efficiency and to grow the business? It is often the case that running on aging DW infrastructure stifles innovation when the demand for data is increasing at an exponential rate.

A massive backlog of BI projects

Often the case in a traditional data warehousing setup. However, what is the cost of critical resources spending hours every week on workaround solutions built-in spreadsheets? Does it have to be the case in 2020s – NO.

Custom scripts to do ETL

Developers love this, and business users hate it. Why? Because they don’t scale, they are not reliable, and when they break, the fixes may not happen on-time. If your developers are writing custom scripts, then you are certainly taking their time away from building innovative solutions.

Multiple applications supporting business operations

Often the case these days with the increased usage of SaaS applications and marketing platforms. This trend is only moving in one direction, and that is upwards. It is more and more common to find best of breed applications in place of monolithic software packages. Quite a few of these applications are replaced more frequently now that they did earlier.

Rapid company growth

A company’s growth intrinsically increases the demands on resource time to deliver more to sustain growth while hiring catches up and new resources ramp up. Ensuring your resources are spending time on activities that directly contribute to growth is vital to maintain this growth and to prevent resource burnout.

Increasing volume and variety of data

Relational databases, document databases, SaaS application, webhooks, files, REST APIs, SOAP APIs, and the varied implementations of each of technologies add a lot of variety and complexity to the tech stack supporting an organization. Handling this complexity is best left to experts who specialize in this area rather than thrusting this role on to an already burdened data engineering and data science teams.

Running resource-intensive queries on production databases

You need to stop now if this is happening.

Manual or spreadsheet-based reporting

Even the creators of excel may not have envisioned the outsized role this software plays in all aspects of the business. Is it convenient, yes! Are you losing out because of this? – quite possibly.

Delays in getting visibility into business metrics

Do your executives have access to business KPIs on their fingertips, or are someone compiling spreadsheets and sharing them via email regularly? Is an important decision delayed as a result?

Increasing demand for predictive analytics

It is no imagination when someone says Data science is in vogue these days, going by the number of companies assembling these resources to tackle critical business challenges. What fun is it if they are spending most of their time extracting data?

Talented yet understaffed business intelligence, analysts, or data science teams

You put together a great team, but you are still unhappy with their delivery. A big reason here could be that their time is being unproductively spent on data wrangling instead of on data modeling and analysis. 

Types of data pipeline solutions

There are a wide variety of data pipeline solutions available in the market today. They cater to different use cases, and each one has its strengths and weaknesses. It is not uncommon for businesses to use a combination of these tools to fulfill their business requirements.

The list below highlights some of the popular types of data pipelines available in the market.

Open Source: Open source solutions have traditionally been the go-to for developers and companies that were trying to avoid the more expensive Enterprise-grade data pipeline solutions. These tools traditional require technical know-how from the team and are often supported only by the community. 

Real-Time or Streaming: As the name suggests, these tools move data from sources to destinations in real-time. Many use cases require the management of real-time data to support various activities around real-time personalization, IoT, financial markets, end telemetry.

Batch: It is common to see applications supporting batch data loads to be in use in a data warehouse environment bear the need for real-time data may not be as acute as it would be in other scenarios. Some use cases for batch loads involve fulfilling demands of the team supporting marketing, sales, customer support, inventory planning, and enterprise reporting. 

Cloud-Native or SaaS: These are the more modern applications that have been designed it support analysts and data scientists in a data-driven company. These applications run in the cloud, offer subscription-based service, and are often more economical than the applications that have been in use traditionally. They also alleviate the burden of management from resources in charge of pipeline maintenance and thereby freeing up their time for more productive applications.

How to get started?

If you find yourself suffering from some of the 12 signs listed above, then it may be time for you to invest in a data pipeline.

Traditional method: Build and maintain an in-house data pipeline. 

Pros: 

  • A sense of accomplishment of having built a solution that fulfills requirements.

Cons:

  • Take a long time to deliver business value
  • Build-out APIs to pull data from a variety of data sources
  • Manage the APIs as they go through various changes throughout the year
  • Build a monitoring system to monitor performance and validity of the data passing through the pipeline
  • Build a notification system that alerts you when there are system or data quality issues
  • Handle changes in the schema in both the destination as well as the source
  • Manage the code base and continue to deliver quality data while resources move in and out of the team
  • Requires expensive resources to build and operate the system

The Daton method:

 Daton offers a more straightforward way to tackle the problem. 

Pros:

  • Replicate your data in minutes not months
  • No need to build-out APIs to pull data from a variety of data sources
  • No management of APIs necessary as they go through various changes throughout the year
  • A built-in monitoring system that monitors the performance and validity of the data passing through the pipeline
  • A built-in notification system that alerts you when there are system or data quality issues
  • Automatically handles changes in the schema in both the destination as well as the source
  • Continuous delivery of quality data 
  • Inexpensive, fast, and scales with your demand.
  • Fully supported by a team of resources 24X7 governed by SLAs
  • Highly secure infrastructure to give you peace of mind

Cons:

  • May not support all of your applications, but that is alleviated as new sources can be added to fulfill your requirements.

Daton is a leading cloud-based, fully-managed, secure, and scalable data pipeline. If you’re ready to learn more about how Daton can help you solve your most significant data extraction and replication challenges, contact us today!

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Krishna

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