Building a scalable data analytics pipeline


All applications, internal and external are generating data. The applications run in isolation until organizations decide on creating a single source of truth by bringing all reasonably important data together and make sense of it. Data, hence, is the vital currency of innovation in all environments. Data-driven decision-making enables organizations to adapt in an unpredictable world of continuous disruptions and growing demands of the market. With the digital world expanding exponentially, data and data analytics systems require a scalable, integrated data pipeline ecosystem. This is a huge challenge in many organizations.

Data Pipeline, in simple terms is a series of activities that move scattered raw data from diverse sources to a destination. In a business environment this source data could be transactional data while destination is where data lakes and data warehouses are created. Data is analysed for actionable insights at the destination.

Kanerika has been at the forefront at offering ETL solutions, creating data lakes and data warehouses, where data is collected, organized, analysed and infused, enabling organizations to build scalable data analytics pipeline.

Why enterprises need a Data Analytics Pipeline?

With the proliferation of different environments like Cloud, it has allowed enterprises to take liberty to onboard different suites of applications, that enable and simplify individual business functions. However, this also results in data silos that are available across different systems and tools. Data silos lead to difficulty in having a single purposed view across the organization. Technology speeds up the process but data silos imbalances the various functions making it a chaotic scenario.

Data Analytics Pipeline or Data Pipelines work towards consolidating all data from different tools and applications to a single destination and helps identify common traits further enabling data analysis and better business insights.

How to build a scalable Data Analytics system?

As your business grows organically, the scalability of your data systems decides the long-term viability of the business. Your hardware and software infrastructure should keep up with the change in data volumes.

Data Capture or Collection (Extract):

Data collection is the first stage of building a data system where organizations needs to assess the origin of data, answering some of the below questions.

  • Where is the data coming from, what is the data source – Applications, API Interfaces, Social media, IOT devices?
  • Will the data be in a structured or unstructured form?
  • What is the amount of data cleaning to be performed?

The architecture will vary based on the method you choose to collect the data – in batch or streaming method. The data will require to go through a Data Serialization process that brings the structured data into a format that allows sharing and storage of data in a form that can be recovered later in the original structure. Data serialization leads to homogenous data structure across the pipeline.

Data Storage (Transform):

Data storage depends on many things, including –

  • Hardware resources
  • Data management expertise
  • Data maintenance

Data is considered as the new oil and hence need to have the best teams own it and manage it. Data infrastructure with a Hadoop File system is the number one choice for the data architecture as it offers tightly integrated ecosystem across all tools and platforms for Data Storage and ETL. Kanerika supports big data architectures such as Amazon AWS and Microsoft Azure.

Here the data goes through –

  • Filtering, cleansing, translations or summaries based on the raw data
  • Performing calculations, translations
  • Removing, encrypting, hiding
  • Formatting data into tables to match the schema of the target data warehouse

Data Warehouse (Load):

The transformed data is moved into respective data warehouses as clean and actionable data which can be further taken up for Analytics, Machine Learning (ML) and Automation. In fact, Automation would usually be the outcome of Analytics as you would have more actionable insights to improve the processes and governance models.

Challenges in creating Data Analytics Pipeline

  • Connections: A modern enterprise is bound to use and add new sources of data as they grow. Every time a new data source is added, the same needs to be integrated into the central system or the pipeline established. Integrations can run into trouble as different systems use different API protocols. These API integrations require first time seamless integration and continuous monitoring. This is necessitated as the data complexity grows along with the business growth.
  • Flexibility or Scalability: The data pipeline should be able to adopt quickly to changes. The changes could be in the form of data or the connections (APIs). Example, data requirement might change based on business process improvements, which require new fields to be introduced. APIs might have been changed by the source system. Hence your build of the system requires such scenarios to be considered.
  • Latency: Data movement from source system to the destination environment should be fast enough to ensure business intelligence is fresh. This can be a challenge if all tools in the pipeline are not geared for real time processing of data.
  • Centralization: When we are talking of a Data Pipeline, we are also talking of having a central IT team to build and maintain the environment. There are a couple of limitations to this
    • Cost of hiring dedicated engineers and
    • Centralization of data processing. This can be solved by using tools like Talend, Xplenty Pentaho.


Once the cleaned-up data is ready in your Data Lake or Data Warehouse, you are ready to dig into those and analyse. The value of data can be experienced once it is unlocked after it is transformed into actionable insight, and when that insight is made available promptly.

Data Pipeline finds new definition when the entire process starts to function independently with minimal interventions. The ETA process combined with the improvements implemented to the processes and systems on a regular basis deliver improved data quality.

Analytical and statistical tools should be used to evaluate the data to discover useful information. With tools like Tableau, Microsoft Power BI, and QlikView organizations can produce graphic visualizations of the analysis and make it available to business stakeholders to take further action. At Kanerika, we have supported our clients on Hadoop, MongoDB, Talend and more.

A Data Analytical Pipeline strategy helps organizations in managing the data end-to-end and gives fast, actionable business insights to all its stakeholders despite employing multiple tools to enable various local functions. Your business leader has real time data for sustainable action.

Kanerika enables you to create data-driven insights to improve your business.
Kanerika enables you to create data-driven insights to improve your business.