Data Pipeline Automation To Create a Data-driven Ecosystem

Data is the lifeblood of any successful business. It powers the decision-making process, helps formulate profitable strategies & serves customers better. For forward-thinking organizations, full automation of data pipelines allows extraction of data at its source to transform & integrate while fueling business applications and data analytics. This is an important aspect of a true data-driven ecosystem.

Gartner suggests that by 2024, 75% of businesses globally will switch from managing data to operationalizing AI, driving a 500% increase in streaming data & analytics infrastructure. And, the ongoing wave of supply chain disruption right now will force many companies to automate data pipelines that would offer to gain insights and visibility to survive.

If you too want to unlock the max potential of your data universe, you must have complete visibility and control of all your data sources. This article will help you understand why data pipeline automation matters and how it can help you optimize customer journeys, innovate products & achieve operational excellence. So, let’s get started!

Key Contents

• Why Data Pipeline Automation Should Be Your Top Priority?
• Rishabh’s Data Pipeline Automation Services Experience
• Data Pipelines Challenges and How We Help Overcome Them
• Data Pipeline Automation Tool Stack

When you list down the differentiating factors that give companies an edge over the competition, a fully automated data pipeline may not pop up as a priority. But to unlock the full potential of your information ecosystem and transform it into real-time insights, you need full control & visibility of data sources and their destinations.

The decision-making quality is directly affected by real-time access to reliable data. Delivering timely, accurate data to business leaders is paramount. And, automation allows businesses to capture meaningful insights and load them into analytics software for real-time analysis and reporting. This helps innovative and agile enterprises to:

• Stay updated with evolving market trends and customer needs
• Create personalized products and services
• Analyze business performance in real-time
• Assess and mitigate risk
• Ensure compliance and business continuity across high-value touchpoints

Advantages Of Data Pipeline Automation

It is a universal fact in the tech landscape that if you manage a process manually, you’ll be able to save money. Or you can create substantial business value by automating it! Here’s how:

1. Reuse & engineer patterns: A deeper understanding of pipeline networks in data processing helps create a way of thinking that sees individual pipes as examples of patterns that can be reused and repurposed for creating new data flows.
2. Fast-track timeline for new data source integration: A shared understanding of tools & processes enables deciding how data should flow through analytics systems. This makes planning seamless for the ingestion of new data sources while reducing the time and cost needed for their integration.
3. Confidence in data quality: The data flow monitoring similar to pipelines enables end-users to improve the data quality and helps reduce the likelihood of pipeline breaks that might go undetected.
4. Incremental build: Thinking about your dataflows as pipelines enable you to grow your dataflows incrementally. By starting with a small manageable slice from a data source to a user, you can start early and gain value quickly.
5. Flexibility and agility: A framework based on pipelines helps respond flexibly to changes in the sources or data users’ needs.

We’ve extensive experience in offering end-to-end data automation for organizations across varied sectors. We help you convert your costly & siloed infrastructure into robust big data pipelines for agile business analytics, machine learning & AI.

It is across;

• Architecture Design: Our specialists assess the project you’re planning or even help you review the existing deployment. By applying industry best practices, and assessing design trade-offs, we ensure that your team’s projects are well designed and built.
• Integration with existing data sources & services: Leverage our specialist focus across every stage of the process: from data collection & data processing to ETL, data cleaning & structuring and finally data visualization to build predictive models on top of the data.
• Implementing Cloud Data Warehousing & ETL: Rishabh team can help implement modern data architectures with a cloud data warehouse or data lake. By adhering to industry best practices, data pipeline development significantly reduces the amount of time spent on data quality processes.

Do explore our data engineering services and solutions to learn how we can help design, build & operationalize your modern data projects to accelerate time-to-value and reduce cost-of-quality.

Success Story – Automation in Action

Our client was a US-based market leader in factoring and trade finance.

Their existing client accounting system had become inefficient in terms of internal data tracking, reporting & automation. This was happening due to technology obsolescence and data integration challenges.

Key Challenges

• Inability to adapt to changing business and technical requirements.
• Lack of business agility in the processes and IT Systems.
• Problems controlling costs of IT and business operations.
• Limited business insight and analytics capabilities in existing IT systems.

Proposed Solution

Rishabh modernized its existing solution in line with the following tenets:

• Technology Stack changes – Build a new application in a new web-based technology stack.
• Framework changes – Upgrade from the old VB6 based framework to the latest tech stack.
• REST API – Created a cross-platform .NET framework for building a modern cloud-based web application.
• Storage – Deployed Microsoft Azure SQL Database to ensure backup, scalability, and high availability.
• Object Storage – Leveraged public & private containers to implement document management.
• Azure SQL Database – Azure Data Factory with SQL change tracking technology to incrementally load delta data from Azure SQL Database into Azure Blob Storage
• Power BI – for generating customer-facing reports, dashboards, and analytics.

Business Impact

• Instant scalability of the cloud at the fraction of the cost of conventional solutions
• Reduced need for developer resources with automated monitoring, management & deployment of analytics
• Quick and cost-effective integration of new data sources
• A competitive edge with better data insights and faster decision making

As businesses pursue their digital transformation journeys, data remains at the core of innovation – from your business strategy & customer experience to marketing, sales & support! So, make no mistake because your ability to leverage this ever-growing volume & variety of data will determine the future success of your business.

But here’s the bottleneck: Managing data pipelines efficiently is not easy!

• Business data is generated in multiple destinations and stored in silos.
• Reports need to be generated from different data sources.
• Most enterprises also struggle with synchronization problems which in turn hampers data consistency.
• With AI gaining traction globally, data sourcing and preparation is becoming a pain point in the absence of automation.
• Running big data workloads in isolation is keeping companies from integrating data-driven ecosystems into their agile & DevOps initiatives.
• Conventional workload automation fails to accommodate the needs of big data workloads & cloud-native infrastructure.
• Missing a single step in the processing data or executing it at the wrong time can result in wasted time and bad data.
• While big data enables fast & informed decision-making, it also poses the problem of integrating big data technologies that cause major operational disruptions and delay the delivery of value.
• Traditional data tools often fail to distribute massive volumes of data to downstream apps in real-time, resulting in slow response times and lost business opportunities.

Fortunately, the solution to overcome all these challenges lies in “Data Pipeline Automation”.

It enables transforming massive volumes of data dumps into actionable insights, more quickly. Here’s How

• Provisioning of computing resources & sandbox for running analytics
• Deploy code & ML models for advanced analytics
• Collect data from several apps & endpoints in real-time
• Organize, process & move data securely
• Ingest data into different big data databases
• Integrate third-party schedulers and open-source tools
• And, finally the use of self-service tools to make data actionable for data engineers