Data & AI Engineering

Transform your data into a competitive advantage.

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Data Platform

We can assist in developing reporting, analytics, and automation solutions tailored to your specific needs, leveraging the full potential of your data.

By harnessing the power of Data and AI, we empower you to uncover insights, predict trends, and make informed decisions, turning your data into a valuable asset for strategic advantage.

Azure and Microsoft's data platform offer a comprehensive and integrated environment designed to meet the diverse data management and analytics needs of modern businesses.

With Azure, clients can harness the power of cloud computing to store, process, and analyze vast amounts of data efficiently. The platform supports a wide range of data types, from structured to unstructured, making it suitable for various applications, from traditional databases to big data analytics and real-time stream processing.

Microsoft's data services are built to provide scalability, security, and compliance, ensuring that businesses can grow and adapt without compromising on critical operational requirements.

Advantages for clients looking to establish a modern data platform in the cloud include:

  • Scalability and Flexibility: Easily scale resources up or down based on demand, and pay only for what you use.
  • Advanced Analytics: Utilize powerful analytics and machine learning tools to uncover insights, predict trends, and make data-driven decisions.
  • Seamless Integration: Integrate with various data sources and services for a cohesive and unified data ecosystem.
  • Global Reach: Deploy and manage your data solutions across multiple regions worldwide, ensuring data locality and compliance.
  • Security and Compliance: Benefit from Azure’s robust security features and compliance certifications to protect your data and meet regulatory requirements.
  • Innovation: Leverage continuous updates and innovations from Microsoft, staying ahead in technology without additional investment in infrastructure.

Modern Data Lake Architecture (Lakehouse)

New systems are beginning to emerge that address the limitations of data lakes. A lakehouse is a new, open architecture that combines the best elements of data lakes and data warehouses. Lakehouses are enabled by a new system design: implementing similar data structures and data management features to those in a data warehouse directly on top of low cost cloud storage in open formats. They are what you would get if you had to redesign data warehouses in the modern world, now that cheap and highly reliable storage (in the form of object stores) are available.

Transaction support: Support for ACID transactions ensures consistency as multiple parties concurrently read or write data, typically using SQL.

Schema enforcement and governance: The Lakehouse should have a way to support schema enforcement and evolution, supporting DW schema architectures such as star/snowflake-schemas.

A lakehouse has the following key features:

BI support: Lakehouses enable using BI tools directly on the source data. This reduces staleness and improves recency, reduces latency, and lowers the cost.

Storage is decoupled from compute: In practice this means storage and compute use separate clusters, thus these systems are able to scale to many more concurrent users and larger data sizes.

Openness: The storage formats they use are open and standardized, such as Parquet, and they provide an API so a variety of tools and engines, including machine learning and Python/R libraries, can efficiently access the data directly.

Support for diverse data types ranging from unstructured to structured data: The lakehouse can be used to store, refine, analyze, and access data types needed for many new data applications, including images, video, audio, semi-structured data, and text.

Support for diverse workloads: including data science, machine learning, and SQL and analytics.

End-to-end streaming: Support for streaming eliminates the need for separate systems dedicated to serving real-time data applications.

Learn more about Databricks

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance.

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Our Execution Model

Process for success

The purpose of cloud product development is to accelerate digital transformation for our clients and improve the experience of users. The powerful combination of shorter development cycles and prioritized user feedback drives product value.

GetTech puts together accountable, passionate, cross-functional teams that work hand in hand with product owners to stay focused, move fast and build better.

Our Team

DevOps culture

There are no silos and there's no blame game, because the team is mutually accountable.

GetTech's squads apply agile DevOps practices and include operations and security in the team responsibilities. Teams work in small batches, focus on improving the end-to-end delivery of customer value, and strive to eliminate waste and impediments along the way.

This allows the team to focus solely on client goals, internalizing a product vision, and taking pride and ownership in their work.

Our Technology

Pioneer tools and technologies

GetTech has always been pioneer of building software and deploying world class cloud technologies. Today that means designing software specifically for the cloud, rather than running a traditional software stack on “someone else’s computers”.

We move our clients beyond “lift and shift” by architecting (and re-architecting) their software to fully leverage cloud deployment.

Big no longer eats small — fast eats slow. Our clients are moving faster than ever thanks to containerization, dynamic orchestration, microservices, and continuous delivery.

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