AWS, Azure, Google Cloud — we're cloud-agnostic but opinionated. These guides offer real talk on choosing the right platform, migrating without downtime, and keeping costs under control before they spiral out of hand.
Moving to the cloud — or picking between data platforms within it — is one of the most consequential decisions a data organization makes. Get it right and you've got a platform that scales with your business. Get it wrong and you're locked into expensive commitments you can't easily escape.
We're platform partners with AWS, Microsoft Azure, and Google Cloud, and we've helped enterprises migrate, modernize, and consolidate across all three. This category shares what we've learned — vendor-neutral, practical, and straight to the point.
Choosing a data platform is a business decision as much as a technical one. Our cloud architects can help you evaluate options against your actual workloads — not a marketing deck.
See How We HelpReal, practical content written by the people who architect cloud platforms for a living. No hype, no fluff — just lessons that pay off.
Two platforms, two philosophies, one big decision. This vendor-agnostic guide breaks down architecture, performance, pricing, and the real-world use cases where each one shines.
Read the guideReconciling data across AWS, Azure, GCP, and on-prem is one of the hardest unsolved problems in data engineering. This guide walks through patterns, schema drift detection, and lakehouse reconciliation.
Read the guidePicking an analytics platform isn't just a tech decision — it's a business decision that affects every team. Here's a practical framework to get it right the first time.
Read the guideYou don't need a tech meltdown to know your data stack is broken. These are the quiet warning signs — and what to do about them before they become expensive emergencies.
Read the guideShort, plain-English definitions of the terms that come up most often.
A cloud data platform is a managed service for storing, processing, and analysing data at scale — such as Snowflake, Databricks, Google BigQuery, Amazon Redshift, or Microsoft Fabric. It separates storage from compute so each can scale independently, and you pay for what you use.
Snowflake is a fully managed, SQL-first data warehouse optimised for analytics and data sharing; Databricks is a lakehouse platform built on Apache Spark that is strongest for data engineering, data science, and machine learning. Both now overlap heavily, so the right choice depends on workloads and team skills.
Multi-cloud data management means running data workloads across more than one cloud provider — for example AWS, Azure, and Google Cloud — while keeping data consistent, governed, and secure across them. Its hardest problems are data drift between environments, differing formats, and cross-cloud reconciliation.
Compute is usually the largest cost: warehouse or cluster size, how long it runs, and inefficient queries. Storage, data egress between regions or clouds, and idle resources follow. Cost control comes from right-sizing, auto-suspend, query optimisation, and usage monitoring.
Choose Snowflake if your priority is SQL analytics, BI, and simple operations; choose Databricks if you need heavy data engineering, streaming, or machine learning on the same platform. Many enterprises use both. The Databricks vs Snowflake comparison covers architecture, performance, pricing, and use cases.
Inventory sources and dependencies, migrate in waves by domain, run old and new systems in parallel, and reconcile outputs between them before cutting over. Automated reconciliation is what proves the migration is correct; 4DAlert compares source and target data across platforms to confirm nothing was lost or changed.
Choose a system of record per domain, standardise formats and keys, schedule continuous cross-cloud reconciliation, and monitor for drift. The cross-cloud reconciliation guide covers multi-cloud and hybrid patterns.
Microsoft Fabric suits organizations already invested in Azure and Power BI, because it unifies data engineering, warehousing, real-time analytics, and BI in one SaaS platform with shared storage (OneLake). Evaluate it against your workloads, governance needs, and existing tools like any other platform.
Performalytic designs and implements data platforms on Snowflake, Databricks, SAP, and Microsoft Fabric, and provides vendor-neutral platform evaluations. See Technologies or request an assessment.
The Knowledge Hub is written by the data engineers, architects, and AI practitioners at Performalytic (performalytic.com) — an enterprise data analytics, AI, and DevOps consulting firm headquartered in Chicago, Illinois, with a global delivery center in Bhubaneswar, India.
Performalytic also builds 4DAlert (4dalert.com), an AI-powered data management platform for automated data reconciliation, data quality and observability, master data management, schema compare, and CI/CD for data.
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