Data Pipeline Services

Build efficient pipelines and automate data ingestion for faster insights

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Integrate data from multiple sources and reduce data latency

To overcome the challenges posed by data silos, Sigmoid’s data pipeline services help to automatically ingest, process, and manage huge volumes of data from diverse sources. We have built over 5000 data pipelines, improved query performance and empowered organizations with faster data access and near real-time visibility to insights. Leveraging our expertise in the end-to-end data engineering ecosystem and open-source technologies, we build flexible ELT solutions by writing cloud-native code. In addition to hand coding data pipelines, Sigmoid builds data pipelines using a combination of no-code, low-code tools and automation.

Guidebook

Building modern data architecture with data lake

Find out how businesses leverage data lakes to capitalize on the available data and drive real-time insights for faster and more effective decision making.

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End-to-end data pipeline and management services

Ingest

Connect siloed data sources faster with our proven frameworks.

Automate

Automate ingestion and data processing from diverse sources.

Streamline

Efficiently process data for real-time reporting and insights.

Migrate

Migrate to the right cloud infrastructure at optimal cost.

Optimize

Improve query performance and enhance scalability.

Govern

Get robust data lineage, security and compliance.

Customer success stories

Our other offerings in data engineering

ML Engineering

Strengthen ML model lifecycle management and accelerate the time to business value for AI projects with robust ML engineering services.

Cloud Transformation

Modernize, migrate, and optimize cloud data performance with agility and reliability for optimal performance and data quality.

DataOps

Managed services to help you automate end-to-end enterprise data infrastructures for agility, high availability, better monitoring, and support.

Insights and perspectives

Blog
ETL on cloud: how is cloud transforming ETL for big data analytics
Infographic
Data lakehouse: combining the best of data lake and data warehouse
Webinar
Reduce AWS costs of high volume ETL pipeline by up to 65%

FAQs

Robust data pipelines notably reduce the average query processing times, resulting in faster insights. Automating data pipelines eliminates the need for manual intervention or adjustments for transferring data between systems.

Businesses can make their data more powerful and execute it in a way that supports progress for tomorrow by using modern data stacks, including tools such as ELT data pipelines and cloud data warehouses.

Before loading the data into the target device, ETL (extract, transform, load) transforms the data at the staging area and redacts sensitive data. The use of streaming ETL reduces the latency of transformations and ETL pipelines can optimize uptime and handle edge cases. Alternatively, ELT (extract, load, transform) loads raw data directly into the target device, where it is transformed. The latency of this pipeline is reduced when there are few or no transformations. A generic edge case solution will result in downtime or increased latency in ELT.

That depends on each case. 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.

Turn data into action faster and drive decision-making at the speed of business.