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Achieve Faster Analytics and reporting through scalable and efficient data pipelines

The one thing common in machine learning (ML), big-data analytics, and data science is that they each need a foundation of data to build over. Almost half the efforts of employees go into preparing data for ML and analytics. Where does this data come from? Through a scalable and efficient data pipeline.

A data pipeline performs the end-to-end operation of data collection, transforming it into insights, model training, delivering those insights, and applying the model to achieve the results wherever necessary. Raw and unrefined data can’t be used profitably. An efficient data pipeline is responsible for transforming that data into actionable insight and making it useful for ML, data analytics, or other operations.

Overview of Big Data Pipeline and Big Data Architecture

A big data pipeline aims to organize the raw data in such a way that it makes reporting, analysis, and usage of data easier. The data is primarily used to improve targeted functionality as well as the BI (business intelligence) and analytics of an organization. The data pipeline refines the raw data captured from different sources and extracts relevant information from it.

There are five stages of a data pipeline that are divided into three categories:

  • Data Engineering: Collection, Ingestion, Preparation
  • Machine learning / Analytics: Computation
  • Delivery: Presentation


Collecting data from different data sources like websites, mobile apps, IoT devices, microservices, and more.


Data is pumped into various inlet points like MQTT, HTTP and more. Data can be imported from services like Google Analytics as well. The data is available in two forms: streams and blobs and they are collected in a Data Lake.


Here the ETL or Extract, Transform, and Load operations are carried out. This operation cleanses, conforms, shapes, transforms, and catalogs the data making it ready to be used in an ML application or stored in a Data Warehouse.


This is where Machine Learning, Data Science, and Analytics happen. A Data Warehouse stores both the structured data and streams


The inferences of the ML model are exposed and the insights are delivered through different channels like microservices, emails, push notifications, and more.

Building a Scalable Big Data Analytics Pipeline

There are three critical factors that help in building a scalable big data analytics pipeline:

Input Data

You must be familiar with your input data’s nature. It is necessary to know the format in which you’re collecting data to determine the format for storing data, what do to when the data goes missing, and what technology would be used in the remainder of the pipeline.

Output Data

Only collecting data is not necessary, you need to think about your end-users as well. These users might not be as technically sound as you are in data engineering and hence the data needs to be easily accessible and understandable. Data Analysts build reporting dashboards or visualizations for the end-users and make the big data ecosystems and analytics warehouse integration a bit easier.

Data Ingestion Capacity

There is a difference between handling 1 GB, 100 GB, and 1 TB of data every day. The long-term viability of your business will depend on how efficiently you can scale your data system. Your hardware and software infrastructure should be able to handle a sudden growth in data volume and be robust enough to keep up with the organic growth of your business.

Path of Data Pipelines Architecture

Data Sources

Data sources are the first layer of the pipeline and a key to its design. They act as the reservoirs where organization gather their data from various different sources. Quality data is needed for the next step that is:


Ingestion is an extraction process that uses Application Programming Interfaces APIs to read data from each source. However, before calling the APIs, you need to perform a step called Profiling where you determine which data you want to extract.

Once the data is profiled for its structure and characteristics, it gets ingested through streaming or as batches.


The structure or format of the extracted data might need to be adjusted. The process of transformation includes filtering, aggregation, mapping coded values, and more.

The timing of any transformation depends on whether the organization prefers ETL (extract, transform, load) or ELT (extract, load, transform). ETL is an older technology and can transform data before its loaded. ELT is modern and can load data without any transformations.


The final destination of data replicated through the pipeline is a data warehouse or data lake. These warehouses contain the cleaned and mastered data of an enterprise in a centralized location. Here analysts and executives can access this rich data and get useful insights.


The entire data pipeline consists of critical components that are subject to failure. Hence it is important for developers to keep monitoring the system constantly, and write logging and alerting code so that any issues can be resolved by the data engineers.

AWS Data Lake and Its Role in the Success of Analytics and Reporting

A data lake is slightly different from a data warehouse as it allows you to store structured as well as unstructured data easily. You can then run different analytics processes like real-time analytics, big data processing, and machine learning to make better decisions.

Amazon AWS data lake provides a cost-effective, scalable, secure, and comprehensive portfolio of services to users so that they can build their data and analyze it efficiently. It even captures data from different IoT devices. Analysts can read this data to improve customer interactions, increase operational efficiency, and improve R&D innovation choices as well.

Agilisium is an advanced AWS data and analytics consulting partner that helps companies with data strategy & consulting, big data analytics, managed services, as well as AWS cloud migration services. They assist with the design, development, and implementation of AWS cloud data projects for different companies.

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“Agilisium architected, designed and delivered an elastically scalable Cloud-based Analytics-ready Big Data solution with AWS S3 Data Lake as the single source of truth”
The client is one of the world’s leading biotechnology company, with presence in 100+ markets globally, was looking for ways to maximize impact of their sales & marketing efforts.

The lack of a single source of truth, quality data and ad hoc manual reporting processes undermined top management’s visibility of integrated insights on sales, sales rep interactions, marketing reach, brand performance, market share, and territory management. Understandably, the client wanted to align information that has hitherto been in silos, to gain a 360-degree product movement view, to optimize sales planning and gain competitive edge.