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Delta Lake on Azure Databricks allows you to configure Delta Lake based on your workload patterns and has optimized layouts and indexes for fast interactive queries. Delta Lake is an open source storage layer that brings reliability to data lakes. It provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of your existing data lake and is fully compatible with Apache Spark APIs.
Successfully building and deploying a machine-learning model can be difficult to do once. Enabling other data scientists (or yourself) to reproduce your pipeline, compare the results of different versions, track what’s running where, and redeploy and rollback updated models is much harder. In this eBook, we’ll explore what makes the ML lifecycle so challenging compared to the traditional software development lifecycle, and share how to address these challenges with Azure Databricks.
In this eBook, we expand, augment and curate on concepts initially published on KDnuggets. In addition, we augment the eBook with assets specific to Delta Lake and Apache Spark 2.x, written and presented by leading Spark contributors and members of Spark PMC including: • Matei Zaharia, the creator of Spark • Reynold Xin, chief architect • Michael Armbrust, lead architect behind Spark SQL and Structured Streaming • Joseph Bradley, one of the drivers behind Spark MLlib and SparkR • Tathagata Das, lead developer for Structured Streaming
This guide walks you through four practical end-to-end machine-learning use cases on Azure Databricks: • A loan risk analysis use case that covers importing and exploring data in Databricks, executing ETL and the ML pipeline, including model tuning with XGBoost Logistic Regression • An advertising analytics and click prediction use case that includes collecting and exploring the advertising logs with Spark SQL and using PySpark for feature engineering and using GBTClassifier for model training and predicting the clicks • A market basket analysis problem at scale, from ETL to data exploration using Spark SQL, and model training using FT-growth • An example of suspicious behavior identification in videos, including a preprocessing step for creating image frames, transfer learning for featurization, and applying logistic regression to identify suspicious images in a video.
Data professionals want to help their organizations innovate for competitive advantage by making the most of their data. Good quality, reliable data forms the foundation for success for such analytics and machine learning initiatives.
Maximise your business intelligence investments by bringing data together from all of your sources with Azure Synapse Analytics. Put your data to work to gain a deeper understanding of your business, and move from reactive reporting to predictive, actionable insights. Read the Packt Publishing e-book, Cloud Analytics with Microsoft Azure, to learn how to:Unify data siloes to deliver insights to all.Enable advanced security control across all data.Achieve unlimited growth into petabyte-scale analytics.Realise unmatched price performance that is up to 14 times faster at a 94 per cent lower cost than other cloud service providers. Learn more about Azure analytics, view real business use cases and get guidance on setting up your limitless cloud data warehouse.
If your company is like many others, you’re exploring ways to get as much value as possible out of the investments you’ve made in your data programme. This may include implementing data security, unifying data silos and providing real-time analytics throughout your organisation. Plan your next move with guidance from analytics customers that have transformed their businesses using a comprehensive data system. In Four ways to maximise your business intelligence investments, a complimentary e-book based on real customer journeys, you’ll learn how to:Get more out of your data investments.Drive corporate innovation with data migration.Unify your data onto a single platform.Bring data insights to everyone.Connect data analysis and profitability. When you’re ready to take the next step in your transformation, you’ll need a provider that delivers breakthrough performance, security and speed. Analytics in Azure is 14 times faster and costs 94 per cent less than other cloud service providers.
Analytics and AI are essential for helping businesses make informed decisions, especially as needs and priorities change rapidly. Learn how to securely manage and scale your organization’s analytics with speed and simplicity—and how to use insights to give your organization a distinct advantage when it comes to decision making. Read Three Ways Analytics Can Help: Respond, Adapt, and Save to discover how to quickly deliver insights from all your data more efficiently with a limitless analytics service that brings together data warehousing and big data analytics. Learn how to: Use BI and AI capabilities in Azure Synapse to access enhanced reporting and dashboard functionality. Make data accessible and deliver critical insights to more people across your organization—including data engineers, data scientists, IT professionals, business analysts, and executives. Safeguard your data with advanced security and privacy features. Get a limitless analytics service that’s up to 14 times faster at a 94 percent lower cost than other cloud providers.
Spend more time building great apps and less time managing server infrastructure. Get your solutions to market faster using Azure Functions, a fully managed compute platform for processing data, integrating systems, and building simple APIs and microservices. In this e-book you’ll find use cases, hands-on steps and tutorials for quickly configuring your own serverless environments. Explore best practices for Functions, and learn how to:Develop event-based handlers on a serverless architecture.Test, troubleshoot and monitor Azure functions.Automate administrative tasks from development through to deployment and maintenance.Integrate functions with other Azure services.Build stateful serverless apps and self-healing jobs using Durable Functions.Download the 399-page serverless computing e-book and get access to dozens of step-by-step recipes for quickly building serverless apps.
Learn how to cut costs and overhead by moving your SQL Server workloads to the cloud with Azure SQL. Find out how four companies from varied industries boosted IT security and reached their unique cloud migration goals using Azure SQL Database, Azure SQL Managed Instance, and SQL Server on Azure Virtual Machines. Read Cloud Lessons Learned: Four Companies That Migrated Their SQL Data to see how moving to Azure helps: Reduce operational, licensing, and infrastructure costs. Improve data security, availability, and disaster recovery. Increase compute, product development, and storage resources. Seamlessly transition diverse workloads to the cloud and manage them there. Boost customer engagement and your ability to adapt to shifts in the market.