Xcelyst enables leading firms to modernize their Data + Decisioning platforms while ensuring Security, Governance & Compliance. Learn more about how we can be your end-to-end partner for all your Data modernization needs.
Implementing a modern Data + Decisioning platform has become an imperative for businesses; especially if they want to leverage advances in Machine Learning and Artificial Intelligence.
These modern data architectures incorporate a scalable, flexible, and cloud-native approach to managing and processing data efficiently. Unlike traditional architectures that rely on rigid, centralized data warehouses, modern data architectures are designed to handle large volumes of structured and unstructured data, support real-time analytics on streaming data, and integrate AI and machine learning seamlessly; while simultaneously ensuring data security, governance and compliance.
At Xcelyst, we offer a comprehensive end-to-end partnership process to help you strategize, architect and implement a modern Data + Decisioning platform.
At Xcelyst, we understand the critical importance of good Data and related high performance Decisioning platforms as a strategic asset to your organization. A modern data architecture allows businesses to move faster, reduce costs, and gain deeper insights. It empowers data teams to build AI-powered applications, process massive datasets efficiently, and support real-time analytics, making it essential for data-driven organizations.
Our comprehensive strategy is designed to guide you through every step of the modernization process, ensuring that your Data + Decisioning platforms enable a powerhouse of innovation, efficiency, and competitive advantage in the global marketplace.
A few of our success stories can be found here.
Data is stored and processed in the cloud (AWS, Azure, GCP, etc), ensuring elastic scaling to handle varying workloads. Serverless and auto-scaling technologies optimize costs and performance
Lakehouses combine the scalability of a data lake with the performance of a data warehouse. They use formats like Delta Lake, Apache Iceberg, or Apache Hudi to ensure ACID transactions and better reliability.
Storage (e.g., AWS S3, Azure Data Lake) is separate from compute (e.g., Databricks, Snowflake, BigQuery). This allows independent scaling, reducing costs and improving performance.
Support real-time streaming (Kafka, Apache Flink) and batch ETL processing (Spark, dbt). Enable instant decision-making and up-to-date data availability.
Avoid vendor lock-in by working across multiple clouds or on-prem + cloud setups. Ensure flexibility and redundancy for global businesses.
Provide native support for machine learning pipelines (MLflow, TensorFlow, AutoML). Enable advanced analytics beyond traditional BI dashboards.
Use metadata-driven governance (Unity Catalog, Apache Atlas) to control access and lineage tracking. Ensure compliance with GDPR, HIPAA, SOC 2 through role-based security and encryption.