Outsourced Data Engineers
Data Engineering and Data Analytics Services
Get nearshore expert help to unlock and understand your data. Find the insights you need to make fast decisions. Manage your massive streams of data.
Our Data Engineering and Data
Science Focused Roles
Our team makes use of SPSS, R, Python, SAS, and Stata to build insightful data models. We are expert at building data visualizations using tools familiar to business users such as Microsoft Power BI, Tableau, Sisense and Qlikview, and developing custom visualizations in Python, R and open source tools such D3.js.
Expertise
Data Engineers
Our data engineers are experts at designing, building, and maintaining data pipelines. They are skilled in a wide range of programming languages and tools, including Python, Java, Spark, Hadoop, and AWS.
Build Data Pipelines
We will build and manage data pipelines. From creating complex flows to integrating with third-party sources, we have seen it all and understand how to build powerful data pipelines
Transform Your Data
Our team of data engineering experts can help you transform your data into organized and normalised insights to drive better decisions. We cleanse, normalize and organize data for analysis
Data Ingestion and Data Quality
Get the the accuracy and reliability you need from your data with our comprehensive data ingestion and quality control
Data Visualization Services
Makes sense of your data by building data visualization assets using Tableau, PowerBI and other tools
Expertise
Our Skilled Data Analysts and Data Scientists Can Unlock the Full Potential of Your Data
Our team of skilled data analysts and data scientists are here to help you fully realize the potential of your data. With their expertise in data modeling, machine learning, and visualization, they can help you extract valuable insights, make data-driven decisions, and drive your business forward. Whether you need support with a specific project or ongoing data strategy consultation, we have the resources and expertise to help you succeed
Robust Data Analysis
Gather reviews and analyze data to find insights and identify patterns and insights quickly
Find Hidden Insights
Leverage our team to think critically and expand the scope of data gathering, discovery and analysis
Expand Your Data Tooling
Understand visualization tools such as Tableau, Microsoft Power BI, Sisense, Qlikview
Advanced Data Mining
Extract value from data through advanced statistical techniques and create data models to find new business opportunities
Deliver instant answers
Identify patterns and make insights quickly. Finally measure business performance consistently
Our people make the difference
Create data models and perform data modeling
Expertise
Machine Learning Engineers (MLOps)
Our MLOps engineers are experts at data science and software engineering. They use a combination of data analysis techniques, machine learning algorithms, and data engineering tools to help companies discover insights from their data at scale. Learn More About Our AI and ML Development
Develop and maintain ML pipelines
Design and implement processes for building, training, and deploying machine learning models.
Integrate ML models into production
Deploy models to servers or cloud environments, set up APIs, and configure monitoring and alerting systems to ensure that the models are running smoothly and accurately.
Automate ML model training and testing
Build automated processes for training and testing ML models on an ongoing basis by setting up scheduled training jobs, building frameworks for testing and evaluating model performance, and implementing continuous integration and delivery processes.
Work with Data Scientists to optimize models
Identify and fix issues by analyzing outputs and training data to identify improvements, implement changes and test results with the broader team.
Monitor and improve performance
Keep track of how well ML model performance in real-world use, and identify and addressing any issues that may arise.
Whether you are looking to make data-driven decisions for your business or simply need help optimizing data analysis processes, there is no better resource than a data expert at Azumo.
When is outsourcing data engineering and analytics the right choice for your business?
At Azumo, we have a team of data engineers, analysts, and scientists who can help you manage and make sense of your most complex data sets. With years of experience working with businesses from a variety of industries, we understand the challenges that arise when dealing with large quantities of data.
Top 5 reasons to hire Azumo for data analysis and data engineering
Data analysts and engineers have the specialized knowledge and skills needed to analyze large amounts of data quickly and efficiently. Whether you need to uncover patterns, trends, or correlations in your data, a data analyst or engineer can help you get the information you need to make informed business decisions.
Our data analysts and engineers have the data visualization skills needed to create compelling data visualizations that make it easy for you to see and understand complex data sets at a glance. This allows you to easily spot trends or anomalies in your data, uncover hidden insights, and make data-driven decisions with confidence.
With our deep understanding of data structures and algorithms, our data analysts and engineers can help you optimize your data architecture for maximum efficiency and performance. This can save you time and money, ensuring that your data is structured efficiently and your queries run really fast.
We are experts at using data mining techniques to uncover hidden patterns, trends, and relationships. Give yourself a competitive edge. Find new opportunities. Uncover hidden risks that you otherwise may miss.
Hiring a data analyst or engineer is an investment in your company’s future. Ultimately we think you will need to own this aspect of your business. But finding skilled data scientists, engineers and analysts can be difficult. With Azumo's help, you can make data-driven decisions that will improve your bottom line, help you gain a competitive edge, and stay ahead of the curve in your industry.
Whether you need to uncover patterns, trends or correlations in your data, our data analysts and engineers will help you get the information you need to make informed business decisions.
No matter what you’re looking for, Azumo has the software development solution for you.
Key Data Infrastructure
We have Deep Expertise across the Cloud Data Warehouse Ecosystem
Snowflake
Snowflake is a data processing engine that is used for data analytics. Snowflake can process data in any format, including structured, unstructured, and streaming data. Snowflake is designed to be fast and scalable, making it an ideal platform for data analytics. With its built-in machine learning algorithms, Snowflake can also be used for predictive analytics. Some of the key features of Snowflake include data warehousing, data integration, data orchestration, data discovery and data virtualization. Overall, Snowflake is a powerful tool for data analysts who need to process large amounts of data quickly and efficiently.
Azure Synapse
Azure Synapse is a data warehouse platform that makes it easy for data engineers, data analysts, and data scientists to store, analyze, and integrate data at scale. Whether you are working with large volumes of enterprise data or exploring data from log files or time-series data streams, Azure Synapse gives you the flexibility to query your data in the way that best suits your needs. With support for a wide range of SQL technologies and tools such as Data Explorer, Spark technologies, and data pipelines, Azure Synapse provides users with all the tools they need to build powerful data warehouses for managing and analyzing their data effectively. In addition, by integrating seamlessly with other Azure services like AzureML and Power BI, Azure Synapse makes it easy to apply advanced analytics techniques to your data and get actionable insights on demand.
Amazon Redshift
Amazon Redshift is a powerful data processing tool used by businesses and organizations of all sizes. It provides immense scalability, allowing users to store and analyze massive amounts of data quickly and easily. Additionally, Amazon Redshift is ideal for cloud-based environments, as it can be accessed from anywhere with an internet connection. This makes it a popular choice for companies that need to perform large-scale data analytics or extract valuable insights from their business operations.
Google BigQuery
BigQuery is a Google cloud platform that enables businesses to run fast, SQL-like queries on large data sets. BigQuery is used for data processing, analysis, and warehousing. BigQuery is serverless, so there is no need to set up or manage any infrastructure. BigQuery is fully managed and scalable, so businesses only pay for the resources they use. BigQuery integrates with other Google Cloud products, making it easy to visualize and analyze data. BigQuery is also compatible with third-party data analytics tools.
Spark
Spark is a data processing engine that is used for data analytics. While not a data warehouse, Spark can process data in any format, including structured data, unstructured data, and streaming data. Spark is designed to be fast and scalable, making it an ideal platform for data analytics. With its built-in machine learning algorithms, Spark can also be used for predictive analytics.