Data Pipelines

ELT/ETL solutions to collate, build and process large volumes of data

Build Scalable & Reliable Data Pipelines

Data silos are a common challenge for companies to develop efficient business strategies. Organizations are looking to extract data from multiple sources, integrate and load them into a data warehouse system to drive better insights. Sigmoid’s ETL & data warehouse solutions enable enterprises with data pipelines to store, process, and manage huge volumes of data from diverse sources at varying speeds. We leverage our expertise in the end-to-end data engineering ecosystem to select or recommend the best of the breed technology stack for building the data pipelines that are robust and significantly reduce the average query processing time, generating faster insights. We have built over 500 data engineering pipelines using pyspark and other ELT tools, including GCP and AWS ETL pipelines for various companies achieving over 60% faster reports and near real-time visibility to customer insights.

What we do

Selecting the right workflow (ETL vs ELT) and data warehousing solution is extremely critical in your data infrastructure. We are pioneers in building scalable and reliable data pipelines and bring about their automation to empower enterprises with faster access to quality data and insights. Sigmoid provides data teams superior query performance through intelligent processing and data management. The data pipeline solutions are customized to specific business needs and the extracted data is transformed for successful loading into the data warehouse on the cloud.

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Connect disparate data sources and systems working in silos

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Automate data ingestion through pipeline optimization

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Enable Data Migration to cloud at scale, speed & optimal cost

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Deliver faster BI through robust ELT Process

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Democratize data providing access to data consumers beyond IT teams

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Modernize data platforms for various analytical needs

Frequently Asked Questions

Robust data pipelines significantly reduce the average query processing times, leading to faster access to insights. Automating data pipelines automates the infrastructure building for transfer of data between systems without the need for manual interventions or adjustments.

ETL (extract, transform, load) transforms the data at the staging area and redact sensitive data before loading it into the target device. Transformations have a higher latency, which can be reduced with streaming ETL. The ETL pipeline can optimize uptime and can be used to manage edge cases. On the other hand, ELT (extract, load, transform) loads the raw data directly into the target device, where the transformation takes place. In this pipeline, the latency is reduced in cases where there are little or no transformations. Edge case solutions that are generic will result in downtime or increased latency in ELT.

The modern data stack, which consists of a suite of tools such as ELT data pipelines, cloud data warehouse for data integration, helps businesses take useful steps to make their business data more powerful and execute it in a way that supports progress for tomorrow. These tools help businesses move toward the data maturity journey.

It depends on a case-to-case basis. ETL tools usually do a good job of moving data from different sources into a relational data warehouse. If that works for you, there’s no urgent need to replace it. However, there are a few scenarios where ELT tools should definitely be considered. For example, an ELT solution may be a better option if your biggest challenges are the increasing volume, velocity and variety of data sources being consumed.

Data Pipeline Case Studies

Read some of our success stories here

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250TB+ data processed for faster customer analytics & building effective data infrastructure

Trade Surveillance

100MN+ rows of data per day
processed for improved trade

Unified Analytics Platform

Unified interactive analytics and
external reporting for enhanced

How Data Engineering Amplifies Business Value of Advanced Analytics

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The whitepaper discusses how well-defined data engineering processes create a robust foundation for consistently delivering insights at scale and overcome big data challenges such as managing data, creating data pipelines, and more.

ETL Data Pipeline Resources

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ETL on Cloud: How is cloud Transforming ETL for Big Data Analytics

Today, organizations are increasingly implementing cloud ETL tools to handle large data sets.

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Amplifying Business Value of Analytics with Cloud Data Warehouse

Cloud data warehouses enable companies with a scalable data infrastructure to handle high-performance analytics workloads.

Learn more about the data engineering services

data warehousing

Cloud Data Warehouse