Data Engineering / Cloud

Scale Your Data Pipeline Efficiently

It can be a struggle to extract business value from your data. Disparate data sources and data quality are just a few of the complex issues you face.

Ensure that the entire pipeline of your data collection yields accurate, consistent results. When you partner with Chariot’s experienced data engineers, you develop highly optimized data pipelines that yield valuable business insights. From collection to analytics to preparation, your pipeline’s plumbing should withstand the digital flood.

AWS Practice Lead Keith Gregory interviews Andrew Ganim about how he helped a multinational company better analyze their data by building a more robust data pipeline.

Chariot Has Expertise In:

Data Lakes

Structuring data and designing queries to a single place, for use by multiple consumers.

Streaming Data

Near-real-time data populates analytics databases and drives customer interactions.

Big Data Databases

Data and queries are designed to yield efficient, cost-effective business intelligence.

Analytics Pipelines

Chariot combines multiple tools and services to acquire, cleanse, transform, and present business data.

Success Stories

The answer is in the architecture. Chariot’s team of experienced data engineers help simplify the process and guide you through the many choices in building the best data pipelines to serve your business needs. We partner with you to build secure, reliable, and highly scalable data pipelines rooted in data integrity.

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Articles, Tutorials, and Writing

Continuous learning is one of our core values here at Chariot, and we believe it’s important to share what we learn. Our data engineers are always writing tutorials, delivering talks, and reviewing the latest new tech. Browse a few pieces of our latest data-focused content here.


Rightsizing Data for Athena

Amazon Athena is a service that lets you run SQL queries against structured data files stored in S3. It takes a “divide and conquer” approach, spinning up parallel query execution engines that each examine only a portion of your data. The performance of these queries, however, depends on how you consolidate and partition your data. In this post I compare query times for a moderately large dataset, looking for the “sweet spot” between number of files and individual file size.

Building Lambdas with Poetry

Coming from a Java background, I consider the Python development process to be a bit of a mess. The pieces are all there: a central repository for publicly-available packages, a way to install the packages you want, and several ways to run your program with only those packages. But it seems that everybody has a different way to combine those pieces. So when a colleague introduced me to Poetry, my first reaction was “oh great, another tool that solves part of my problem.” But after spending time with it, I don’t want to build Lambdas any other way.

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AWS offers so many products for cloud computing that it takes an expert to understand which tools are best for your business process. Let our team of expert AWS consultants guide you through selection, implementation, maintenance and security.
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Our Process

Collection → Ingestion → Preparation → Analytics/Data Science/ML → Presentation

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