Building a scalable data analytics pipeline

Introduction

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