#what is databricks

[digitalmarketing6669]

Unlock the Future of ML with Azure Databricks – Here's Why You Should Care

[xiaq]

Tech conferences are not for me.

For the last two days I've been helping to run a demo booth for my company at the Databricks summit and while I really enjoy the customer interaction and demoing and learning from my peers, the sheer quantity of stimulus from 8am to 8pm is breaking my brain.

On the expo floor they play club remixes of popular songs at high volume all day, so your baseline is shouting. And then you have thousands of people all shouting over each other on top of that. And the overhead lights are hella bright and most of the booths have LED lights and all the monitors are glaring to the point that I feel like the lights all have sounds too.

Tonight I got back to my hotel room with my takeaway dinner and I was so relieved to be in a quiet place I literally started crying. I hadn't eaten since breakfast because there weren't GF lunch options, though, so I didn't stop eating my tofu and rice noodles. I was just ugly sobbing while trying to stuff my face.

And what's wild is that most of my coworkers were headed to dinner together at a bar/restaurant AFTER scream-talking for 10 hours that day.

Like, sure my brain isn't normal but I feel like that's an excessive amount of socializing even for neurotypical people.

Anyway, me and my vendor swag homies are going to watch some HGTV and go to sleep early because we've got to do it all over again tomorrow.

[mariacallous]

Multiple current and former government IT sources tell WIRED that it would be easy to connect the IRS’s Palantir system with the ICE system at DHS, allowing users to query data from both systems simultaneously. A system like the one being created at the IRS with Palantir could enable near-instantaneous access to tax information for use by DHS and immigration enforcement. It could also be leveraged to share and query data from different agencies as well, including immigration data from DHS. Other DHS sub-agencies, like USCIS, use Databricks software to organize and search its data, but these could be connected to outside Foundry instances simply as well, experts say. Last month, Palantir and Databricks struck a deal making the two software platforms more interoperable.

“I think it's hard to overstate what a significant departure this is and the reshaping of longstanding norms and expectations that people have about what the government does with their data,” says Elizabeth Laird, director of equity in civic technology at the Center for Democracy and Technology, who noted that agencies trying to match different datasets can also lead to errors. “You have false positives and you have false negatives. But in this case, you know, a false positive where you're saying someone should be targeted for deportation.”

Mistakes in the context of immigration can have devastating consequences: In March, authorities arrested and deported Kilmar Abrego Garcia, a Salvadoran national, due to, the Trump administration says, “an administrative error.” Still, the administration has refused to bring Abrego Garcia back, defying a Supreme Court ruling.

“The ultimate concern is a panopticon of a single federal database with everything that the government knows about every single person in this country,” Venzke says. “What we are seeing is likely the first step in creating that centralized dossier on everyone in this country.”

DOGE Is Building a Master Database to Surveil and Track Immigrants

[darkmaga-returns]

What EDAV does:

Connects people with data faster. It does this in a few ways. EDAV:

Hosts tools that support the analytics work of over 3,500 people.

Stores data on a common platform that is accessible to CDC's data scientists and partners.

Simplifies complex data analysis steps.

Automates repeatable tasks, such as dashboard updates, freeing up staff time and resources.

Keeps data secure. Data represent people, and the privacy of people's information is critically important to CDC. EDAV is hosted on CDC's Cloud to ensure data are shared securely and that privacy is protected.

Saves time and money. EDAV services can quickly and easily scale up to meet surges in demand for data science and engineering tools, such as during a disease outbreak. The services can also scale down quickly, saving funds when demand decreases or an outbreak ends.

Trains CDC's staff on new tools. EDAV hosts a Data Academy that offers training designed to help our workforce build their data science skills, including self-paced courses in Power BI, R, Socrata, Tableau, Databricks, Azure Data Factory, and more.

Changes how CDC works. For the first time, EDAV offers CDC's experts a common set of tools that can be used for any disease or condition. It's ready to handle "big data," can bring in entirely new sources of data like social media feeds, and enables CDC's scientists to create interactive dashboards and apply technologies like artificial intelligence for deeper analysis.

[algoworks]

Move over, Salesforce and Microsoft! Databricks is shaking things up with their game-changing AI/BI tool. Get ready for smarter, faster insights that leave the competition in the dust.

Who's excited to see what this powerhouse can do?

[caffeineandviolins]

PART TWO

The six men are one part of the broader project of Musk allies assuming key government positions. Already, Musk’s lackeys—including more senior staff from xAI, Tesla, and the Boring Company—have taken control of the Office of Personnel Management (OPM) and General Services Administration (GSA), and have gained access to the Treasury Department’s payment system, potentially allowing him access to a vast range of sensitive information about tens of millions of citizens, businesses, and more. On Sunday, CNN reported that DOGE personnel attempted to improperly access classified information and security systems at the US Agency for International Development and that top USAID security officials who thwarted the attempt were subsequently put on leave. The Associated Press reported that DOGE personnel had indeed accessed classified material.“What we're seeing is unprecedented in that you have these actors who are not really public officials gaining access to the most sensitive data in government,” says Don Moynihan, a professor of public policy at the University of Michigan. “We really have very little eyes on what's going on. Congress has no ability to really intervene and monitor what's happening because these aren't really accountable public officials. So this feels like a hostile takeover of the machinery of governments by the richest man in the world.”Bobba has attended UC Berkeley, where he was in the prestigious Management, Entrepreneurship, and Technology program. According to a copy of his now-deleted LinkedIn obtained by WIRED, Bobba was an investment engineering intern at the Bridgewater Associates hedge fund as of last spring and was previously an intern at both Meta and Palantir. He was a featured guest on a since-deleted podcast with Aman Manazir, an engineer who interviews engineers about how they landed their dream jobs, where he talked about those experiences last June.

Coristine, as WIRED previously reported, appears to have recently graduated from high school and to have been enrolled at Northeastern University. According to a copy of his résumé obtained by WIRED, he spent three months at Neuralink, Musk’s brain-computer interface company, last summer.Both Bobba and Coristine are listed in internal OPM records reviewed by WIRED as “experts” at OPM, reporting directly to Amanda Scales, its new chief of staff. Scales previously worked on talent for xAI, Musk’s artificial intelligence company, and as part of Uber’s talent acquisition team, per LinkedIn. Employees at GSA tell WIRED that Coristine has appeared on calls where workers were made to go over code they had written and justify their jobs. WIRED previously reported that Coristine was added to a call with GSA staff members using a nongovernment Gmail address. Employees were not given an explanation as to who he was or why he was on the calls.

Farritor, who per sources has a working GSA email address, is a former intern at SpaceX, Musk’s space company, and currently a Thiel Fellow after, according to his LinkedIn, dropping out of the University of Nebraska—Lincoln. While in school, he was part of an award-winning team that deciphered portions of an ancient Greek scroll.AdvertisementKliger, whose LinkedIn lists him as a special adviser to the director of OPM and who is listed in internal records reviewed by WIRED as a special adviser to the director for information technology, attended UC Berkeley until 2020; most recently, according to his LinkedIn, he worked for the AI company Databricks. His Substack includes a post titled “The Curious Case of Matt Gaetz: How the Deep State Destroys Its Enemies,” as well as another titled “Pete Hegseth as Secretary of Defense: The Warrior Washington Fears.”Killian, also known as Cole Killian, has a working email associated with DOGE, where he is currently listed as a volunteer, according to internal records reviewed by WIRED. According to a copy of his now-deleted résumé obtained by WIRED, he attended McGill University through at least 2021 and graduated high school in 2019. An archived copy of his now-deleted personal website indicates that he worked as an engineer at Jump Trading, which specializes in algorithmic and high-frequency financial trades.Shaotran told Business Insider in September that he was a senior at Harvard studying computer science and also the founder of an OpenAI-backed startup, Energize AI. Shaotran was the runner-up in a hackathon held by xAI, Musk’s AI company. In the Business Insider article, Shaotran says he received a $100,000 grant from OpenAI to build his scheduling assistant, Spark.

Are you a current or former employee with the Office of Personnel Management or another government agency impacted by Elon Musk? We’d like to hear from you. Using a nonwork phone or computer, contact Vittoria Elliott at vittoria_elliott@wired.com or securely at velliott88.18 on Signal.“To the extent these individuals are exercising what would otherwise be relatively significant managerial control over two very large agencies that deal with very complex topics,” says Nick Bednar, a professor at University of Minnesota’s school of law, “it is very unlikely they have the expertise to understand either the law or the administrative needs that surround these agencies.”Sources tell WIRED that Bobba, Coristine, Farritor, and Shaotran all currently have working GSA emails and A-suite level clearance at the GSA, which means that they work out of the agency’s top floor and have access to all physical spaces and IT systems, according a source with knowledge of the GSA’s clearance protocols. The source, who spoke to WIRED on the condition of anonymity because they fear retaliation, says they worry that the new teams could bypass the regular security clearance protocols to access the agency’s sensitive compartmented information facility, as the Trump administration has already granted temporary security clearances to unvetted people.This is in addition to Coristine and Bobba being listed as “experts” working at OPM. Bednar says that while staff can be loaned out between agencies for special projects or to work on issues that might cross agency lines, it’s not exactly common practice.“This is consistent with the pattern of a lot of tech executives who have taken certain roles of the administration,” says Bednar. “This raises concerns about regulatory capture and whether these individuals may have preferences that don’t serve the American public or the federal government.”

These men just stole the personal information of everyone in America AND control the Treasury. Link to article.

Akash Bobba

Edward Coristine

Luke Farritor

Gautier Cole Killian

Gavin Kliger

Ethan Shaotran

Spread their names!

[kamalkafir-blog]

TechOps - DE - CloudOps - DataOps - Senior

Job title: TechOps – DE – CloudOps – DataOps – Senior Company: EY Job description: . Experience in industries such as retail, finance, or consumer goods. Certifications such as: Informatica Certified Developer… Microsoft Certified: Azure Data Engineer Associate Databricks Certified Data Engineer What you will do: Provide daily… Expected salary: Location: Kochi, Kerala Job date: Sat, 03 May 2025…

[xaltius]

The top Data Engineering trends to look for in 2025

Data engineering is the unsung hero of our data-driven world. It's the critical discipline that builds and maintains the robust infrastructure enabling organizations to collect, store, process, and analyze vast amounts of data. As we navigate mid-2025, this foundational field is evolving at an unprecedented pace, driven by the exponential growth of data, the insatiable demand for real-time insights, and the transformative power of AI.

Staying ahead of these shifts is no longer optional; it's essential for data engineers and the organizations they support. Let's dive into the key data engineering trends that are defining the landscape in 2025.

1. The Dominance of the Data Lakehouse

What it is: The data lakehouse architecture continues its strong upward trajectory, aiming to unify the best features of data lakes (flexible, low-cost storage for raw, diverse data types) and data warehouses (structured data management, ACID transactions, and robust governance). Why it's significant: It offers a single platform for various analytics workloads, from BI and reporting to AI and machine learning, reducing data silos, complexity, and redundancy. Open table formats like Apache Iceberg, Delta Lake, and Hudi are pivotal in enabling lakehouse capabilities. Impact: Greater data accessibility, improved data quality and reliability for analytics, simplified data architecture, and cost efficiencies. Key Technologies: Databricks, Snowflake, Amazon S3, Azure Data Lake Storage, Apache Spark, and open table formats.

2. AI-Powered Data Engineering (Including Generative AI)

What it is: Artificial intelligence, and increasingly Generative AI, are becoming integral to data engineering itself. This involves using AI/ML to automate and optimize various data engineering tasks. Why it's significant: AI can significantly boost efficiency, reduce manual effort, improve data quality, and even help generate code for data pipelines or transformations. Impact: * Automated Data Integration & Transformation: AI tools can now automate aspects of data mapping, cleansing, and pipeline optimization. * Intelligent Data Quality & Anomaly Detection: ML algorithms can proactively identify and flag data quality issues or anomalies in pipelines. * Optimized Pipeline Performance: AI can help in tuning and optimizing the performance of data workflows. * Generative AI for Code & Documentation: LLMs are being used to assist in writing SQL queries, Python scripts for ETL, and auto-generating documentation. Key Technologies: AI-driven ETL/ELT tools, MLOps frameworks integrated with DataOps, platforms with built-in AI capabilities (e.g., Databricks AI Functions, AWS DMS with GenAI).

3. Real-Time Data Processing & Streaming Analytics as the Norm

What it is: The demand for immediate insights and actions based on live data streams continues to grow. Batch processing is no longer sufficient for many use cases. Why it's significant: Businesses across industries like e-commerce, finance, IoT, and logistics require real-time capabilities for fraud detection, personalized recommendations, operational monitoring, and instant decision-making. Impact: A shift towards streaming architectures, event-driven data pipelines, and tools that can handle high-throughput, low-latency data. Key Technologies: Apache Kafka, Apache Flink, Apache Spark Streaming, Apache Pulsar, cloud-native streaming services (e.g., Amazon Kinesis, Google Cloud Dataflow, Azure Stream Analytics), and real-time analytical databases.

4. The Rise of Data Mesh & Data Fabric Architectures

What it is: * Data Mesh: A decentralized sociotechnical approach that emphasizes domain-oriented data ownership, treating data as a product, self-serve data infrastructure, and federated computational governance. * Data Fabric: An architectural approach that automates data integration and delivery across disparate data sources, often using metadata and AI to provide a unified view and access to data regardless of where it resides. Why it's significant: Traditional centralized data architectures struggle with the scale and complexity of modern data. These approaches offer greater agility, scalability, and empower domain teams. Impact: Improved data accessibility and discoverability, faster time-to-insight for domain teams, reduced bottlenecks for central data teams, and better alignment of data with business domains. Key Technologies: Data catalogs, data virtualization tools, API-based data access, and platforms supporting decentralized data management.

5. Enhanced Focus on Data Observability & Governance

What it is: * Data Observability: Going beyond traditional monitoring to provide deep visibility into the health and state of data and data pipelines. It involves tracking data lineage, quality, freshness, schema changes, and distribution. * Data Governance by Design: Integrating robust data governance, security, and compliance practices directly into the data lifecycle and infrastructure from the outset, rather than as an afterthought. Why it's significant: As data volumes and complexity grow, ensuring data quality, reliability, and compliance (e.g., GDPR, CCPA) becomes paramount for building trust and making sound decisions. Regulatory landscapes, like the EU AI Act, are also making strong governance non-negotiable. Impact: Improved data trust and reliability, faster incident resolution, better compliance, and more secure data handling. Key Technologies: AI-powered data observability platforms, data cataloging tools with governance features, automated data quality frameworks, and tools supporting data lineage.

6. Maturation of DataOps and MLOps Practices

What it is: * DataOps: Applying Agile and DevOps principles (automation, collaboration, continuous integration/continuous delivery - CI/CD) to the entire data analytics lifecycle, from data ingestion to insight delivery. * MLOps: Extending DevOps principles specifically to the machine learning lifecycle, focusing on streamlining model development, deployment, monitoring, and retraining. Why it's significant: These practices are crucial for improving the speed, quality, reliability, and efficiency of data and machine learning pipelines. Impact: Faster delivery of data products and ML models, improved data quality, enhanced collaboration between data engineers, data scientists, and IT operations, and more reliable production systems. Key Technologies: Workflow orchestration tools (e.g., Apache Airflow, Kestra), CI/CD tools (e.g., Jenkins, GitLab CI), version control systems (Git), containerization (Docker, Kubernetes), and MLOps platforms (e.g., MLflow, Kubeflow, SageMaker, Azure ML).

The Cross-Cutting Theme: Cloud-Native and Cost Optimization

Underpinning many of these trends is the continued dominance of cloud-native data engineering. Cloud platforms (AWS, Azure, GCP) provide the scalable, flexible, and managed services that are essential for modern data infrastructure. Coupled with this is an increasing focus on cloud cost optimization (FinOps for data), as organizations strive to manage and reduce the expenses associated with large-scale data processing and storage in the cloud.

The Evolving Role of the Data Engineer

These trends are reshaping the role of the data engineer. Beyond building pipelines, data engineers in 2025 are increasingly becoming architects of more intelligent, automated, and governed data systems. Skills in AI/ML, cloud platforms, real-time processing, and distributed architectures are becoming even more crucial.

Global Relevance, Local Impact

These global data engineering trends are particularly critical for rapidly developing digital economies. In countries like India, where the data explosion is immense and the drive for digital transformation is strong, adopting these advanced data engineering practices is key to harnessing data for innovation, improving operational efficiency, and building competitive advantages on a global scale.

Conclusion: Building the Future, One Pipeline at a Time

The field of data engineering is more dynamic and critical than ever. The trends of 2025 point towards more automated, real-time, governed, and AI-augmented data infrastructures. For data engineering professionals and the organizations they serve, embracing these changes means not just keeping pace, but actively shaping the future of how data powers our world.

[kadellabs69]

Unlocking the Power of Delta Live Tables in Data bricks with Kadel Labs

Introduction

In the rapidly evolving landscape of big data and analytics, businesses are constantly seeking ways to streamline data processing, ensure data reliability, and improve real-time analytics. One of the most powerful solutions available today is Delta Live Tables (DLT) in Databricks. This cutting-edge feature simplifies data engineering and ensures efficiency in data pipelines.

Kadel Labs, a leader in digital transformation and data engineering solutions, leverages Delta Live Tables to optimize data workflows, ensuring businesses can harness the full potential of their data. In this article, we will explore what Delta Live Tables are, how they function in Databricks, and how Kadel Labs integrates this technology to drive innovation.

Understanding Delta Live Tables

What Are Delta Live Tables?

Delta Live Tables (DLT) is an advanced framework within Databricks that simplifies the process of building and maintaining reliable ETL (Extract, Transform, Load) pipelines. With DLT, data engineers can define incremental data processing pipelines using SQL or Python, ensuring efficient data ingestion, transformation, and management.

Key Features of Delta Live Tables

Automated Pipeline Management

DLT automatically tracks changes in source data, eliminating the need for manual intervention.

Data Reliability and Quality

Built-in data quality enforcement ensures data consistency and correctness.

Incremental Processing

Instead of processing entire datasets, DLT processes only new data, improving efficiency.

Integration with Delta Lake

DLT is built on Delta Lake, ensuring ACID transactions and versioned data storage.

Monitoring and Observability

With automatic lineage tracking, businesses gain better insights into data transformations.

How Delta Live Tables Work in Databricks

Databricks, a unified data analytics platform, integrates Delta Live Tables to streamline data lake house architectures. Using DLT, businesses can create declarative ETL pipelines that are easy to maintain and highly scalable.

The DLT Workflow

Define a Table and Pipeline

Data engineers specify data sources, transformation logic, and the target Delta table.

Data Ingestion and Transformation

DLT automatically ingests raw data and applies transformation logic in real-time.

Validation and Quality Checks

DLT enforces data quality rules, ensuring only clean and accurate data is processed.

Automatic Processing and Scaling

Databricks dynamically scales resources to handle varying data loads efficiently.

Continuous or Triggered Execution

DLT pipelines can run continuously or be triggered on-demand based on business needs.

Kadel Labs: Enhancing Data Pipelines with Delta Live Tables

As a digital transformation company, Kadel Labs specializes in deploying cutting-edge data engineering solutions that drive business intelligence and operational efficiency. The integration of Delta Live Tables in Databricks is a game-changer for organizations looking to automate, optimize, and scale their data operations.

How Kadel Labs Uses Delta Live Tables

Real-Time Data Streaming

Kadel Labs implements DLT-powered streaming pipelines for real-time analytics and decision-making.

Data Governance and Compliance

By leveraging DLT’s built-in monitoring and validation, Kadel Labs ensures regulatory compliance.

Optimized Data Warehousing

DLT enables businesses to build cost-effective data warehouses with improved data integrity.

Seamless Cloud Integration

Kadel Labs integrates DLT with cloud environments (AWS, Azure, GCP) to enhance scalability.

Business Intelligence and AI Readiness

DLT transforms raw data into structured datasets, fueling AI and ML models for predictive analytics.

Benefits of Using Delta Live Tables in Databricks

1. Simplified ETL Development

With DLT, data engineers spend less time managing complex ETL processes and more time focusing on insights.

2. Improved Data Accuracy and Consistency

DLT automatically enforces quality checks, reducing errors and ensuring data accuracy.

3. Increased Operational Efficiency

DLT pipelines self-optimize, reducing manual workload and infrastructure costs.

4. Scalability for Big Data

DLT seamlessly scales based on workload demands, making it ideal for high-volume data processing.

5. Better Insights with Lineage Tracking

Data lineage tracking in DLT provides full visibility into data transformations and dependencies.

Real-World Use Cases of Delta Live Tables with Kadel Labs

1. Retail Analytics and Customer Insights

Kadel Labs helps retailers use Delta Live Tables to analyze customer behavior, sales trends, and inventory forecasting.

2. Financial Fraud Detection

By implementing DLT-powered machine learning models, Kadel Labs helps financial institutions detect fraudulent transactions.

3. Healthcare Data Management

Kadel Labs leverages DLT in Databricks to improve patient data analysis, claims processing, and medical research.

4. IoT Data Processing

For smart devices and IoT applications, DLT enables real-time sensor data processing and predictive maintenance.

Conclusion

Delta Live Tables in Databricks is transforming the way businesses handle data ingestion, transformation, and analytics. By partnering with Kadel Labs, companies can leverage DLT to automate pipelines, improve data quality, and gain actionable insights.

With its expertise in data engineering, Kadel Labs empowers businesses to unlock the full potential of Databricks and Delta Live Tables, ensuring scalable, efficient, and reliable data solutions for the future.

For businesses looking to modernize their data architecture, now is the time to explore Delta Live Tables with Kadel Labs!

[dataplatr-1]

The Power of Managed Analytics Services for Smarter Business Decisions

At Dataplatr, we empower organizations to make smarter, faster, and more strategic decisions by delivering Customized managed ata analytics services that align with specific business goals. With the rising complexity of data ecosystems, many organizations struggle with siloed information, lack of real-time insights, and underutilized analytics tools. Managed data services from Dataplatr eliminate these challenges by offering end-to-end support—from data integration to advanced analytics and visualization. Our approach helps streamline data operations and achieve the true value of business intelligence.

What Are Managed Analytics Services?

Managed analytics services are comprehensive solutions designed to handle everything from data integration and storage to analytics, visualization, and insight generation. At Dataplatr, we offer end-to-end managed data analytics services tailored to meet your unique business needs—helping you gain clarity, agility, and efficiency.

The Challenge: Making Sense of Growing Data Complexity

Organizations often find themselves overwhelmed with data. Without the right infrastructure and expertise, valuable insights remain buried. This is where data analytics managed services from Dataplatr become crucial. We help businesses streamline their data ecosystems, ensuring every byte is aligned with strategic objectives.

The Role of Managed Data Analytics Services in Strategic Thinking

Data Democratization - Make insights accessible across departments for more aligned decision-making.

Real-Time Visibility - Monitor KPIs and trends with live dashboards powered by data analytics managed services.

Predictive Capabilities - Identify future opportunities and mitigate risks using predictive models powered by Dataplatr.

Managed Data Services: The Foundation for Data-Driven Success

At Dataplatr, our managed data services lay the groundwork for long-term analytics maturity. From data ingestion and cleansing to transformation and storage, we handle the full data lifecycle. This enables our clients to build reliable, high-quality datasets that fuel more accurate insights and smarter decisions.

From Raw Data to Business Intelligence

Our managed data analytics services are designed to turn raw data into strategic assets. By using modern tools and platforms like Databricks, Snowflake, and Looker, we help organizations visualize key trends, uncover patterns, and respond proactively to market demands. This 

Smarter Decisions Start Here

Partnering with Dataplatr means tapping into a new era of decision-making—powered by intelligent, scalable, and efficient analytics. Our managed data analytics services simplify complexity, eliminate inefficiencies, and drive real business value.

[cloudthathh]

Understanding DP-900: Microsoft Azure Data Fundamentals

The DP-900, or Microsoft Azure Data Fundamentals, is an entry-level certification designed for individuals looking to build foundational knowledge of core data concepts and Microsoft Azure data services. This certification validates a candidate’s understanding of relational and non-relational data, data workloads, and the basics of data processing in the cloud. It serves as a stepping stone for those pursuing more advanced Azure data certifications, such as the DP-203 (Azure Data Engineer Associate) or the DP-300 (Azure Database Administrator Associate).

What Is DP-900?

The DP-900 exam, officially titled "Microsoft Azure Data Fundamentals," tests candidates on fundamental data concepts and how they are implemented using Microsoft Azure services. It is part of Microsoft’s role-based certification path, specifically targeting beginners who want to explore data-related roles in the cloud. The exam does not require prior experience with Azure, making it accessible to students, career changers, and IT professionals new to cloud computing.

Exam Objectives and Key Topics

The DP-900 exam covers four primary domains:

1. Core Data Concepts (20-25%) - Understanding relational and non-relational data. - Differentiating between transactional and analytical workloads. - Exploring data processing options (batch vs. real-time).

2. Working with Relational Data on Azure (25-30%) - Overview of Azure SQL Database, Azure Database for PostgreSQL, and Azure Database for MySQL. - Basic provisioning and deployment of relational databases. - Querying data using SQL.

3. Working with Non-Relational Data on Azure (25-30%) - Introduction to Azure Cosmos DB and Azure Blob Storage. - Understanding NoSQL databases and their use cases. - Exploring file, table, and graph-based data storage.

4. Data Analytics Workloads on Azure (20-25%) - Basics of Azure Synapse Analytics and Azure Databricks. - Introduction to data visualization with Power BI. - Understanding data ingestion and processing pipelines.

Who Should Take the DP-900 Exam?

The DP-900 certification is ideal for: - Beginners with no prior Azure experience who want to start a career in cloud data services. - IT Professionals looking to validate their foundational knowledge of Azure data solutions. - Students and Career Changers exploring opportunities in data engineering, database administration, or analytics. - Business Stakeholders who need a high-level understanding of Azure data services to make informed decisions.

Preparation Tips for the DP-900 Exam

1. Leverage Microsoft’s Free Learning Resources Microsoft offers free online training modules through Microsoft Learn, covering all exam objectives. These modules include hands-on labs and interactive exercises.

2. Practice with Hands-on Labs Azure provides a free tier with limited services, allowing candidates to experiment with databases, storage, and analytics tools. Practical experience reinforces theoretical knowledge.

3. Take Practice Tests Practice exams help identify weak areas and familiarize candidates with the question format. Websites like MeasureUp and Whizlabs offer DP-900 practice tests.

4. Join Study Groups and Forums Online communities, such as Reddit’s r/AzureCertification or Microsoft’s Tech Community, provide valuable insights and study tips from past exam takers.

5. Review Official Documentation Microsoft’s documentation on Azure data services is comprehensive and frequently updated. Reading through key concepts ensures a deeper understanding.

Benefits of Earning the DP-900 Certification

1. Career Advancement The certification demonstrates foundational expertise in Azure data services, making candidates more attractive to employers.

2. Pathway to Advanced Certifications DP-900 serves as a prerequisite for higher-level Azure data certifications, helping professionals specialize in data engineering or database administration.

3. Industry Recognition Microsoft certifications are globally recognized, adding credibility to a resume and increasing job prospects.

4. Skill Validation Passing the exam confirms a solid grasp of cloud data concepts, which is valuable in roles involving data storage, processing, or analytics.

Exam Logistics

- Exam Format: Multiple-choice questions (single and multiple responses). - Duration: 60 minutes. - Passing Score: 700 out of 1000. - Languages Available: English, Japanese, Korean, Simplified Chinese, and more. - Cost: $99 USD (prices may vary by region).

Conclusion

The DP-900 Microsoft Azure Data Fundamentals certification is an excellent starting point for anyone interested in cloud-based data solutions. By covering core data concepts, relational and non-relational databases, and analytics workloads, it provides a well-rounded introduction to Azure’s data ecosystem. With proper preparation, candidates can pass the exam and use it as a foundation for more advanced certifications. Whether you’re a student, IT professional, or business stakeholder, earning the DP-900 certification can open doors to new career opportunities in the growing field of cloud data management.

[hubertdudek]

Databricks: what’s new in April 2025? Updates & Features Explained! #databricks Databricks, What’s New in Databricks? April 2025 Updates & Features Explained! 📌 Key Highlights for This Month: - *00:04* PowerBI task - Refresh PowerBI from Databricks - *01:36* SQL task values - Pass SELECT result to workflow - *05:38* Cost-optimized jobs - Serverless standard mode - *06:34* Google Sheets - Query Databricks - *07:48* Git for dashboards - *08:38* Genie sampling - Genie can read data - *11:22* UC functions with PyPl libraries - *12:22* Anomaly detection - *15:02* PII scanner - Data classification - *16:13* Turn off Hive metastore - *17:17* AI builder - Extract data and more - *21:12* AI query with schema - *22:41* PyDABS - *23:28* ALTER statement - *24:03* TEMP VIEWS in DLT - *24:18* Apps on behalf of the user ============================= 📚 *Notebooks from the video:* 🔗 [GitHub Repository](https://ift.tt/S13qG0b) 🔔𝐃𝐨𝐧'𝐭 𝐟𝐨𝐫𝐠𝐞𝐭 𝐭𝐨 𝐬𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐜𝐡𝐚𝐧𝐧𝐞𝐥 𝐟𝐨𝐫 𝐦𝐨𝐫𝐞 𝐮𝐩𝐝𝐚𝐭𝐞𝐬. https://www.youtube.com/@hubert_dudek/?sub_confirmation=1 🔗 Support Me Here! ☕Buy me a coffee: https://ift.tt/9qIpuET ✨ Explore Databricks AI insights and workflows—read more: https://ift.tt/1djZykN ============================= 🎬Suggested videos for you: ▶️ [What’s new in January 2025](https://www.youtube.com/watch?v=JJiwSplZmfk) ▶️ [What’s new in February 2025](https://www.youtube.com/watch?v=tuKI0sBNbmg) ▶️ [What’s new in March 2025](https://youtu.be/hJD7KoNq-uE) ============================= 📚 **New Articles for Further Reading:** - 📝 *More on Databricks into Google Sheets:* 🔗 [Read the full article](https://ift.tt/3cfjJLy) - 📝 *More on Anomaly Detection & Data Freshness:* 🔗 [Read the full article](https://ift.tt/5RB4bWM) - 📝 *More on Goodbye to Hive Metastore:* 🔗 [Read the full article](https://ift.tt/lxjpoRS) - 📝 *More on Databricks Refresh PowerBI Semantic Model:* 🔗 [Read the full article](https://ift.tt/8JAfSvZ) - 📝 *More on ResponseFormat in AI Batch Inference:* 🔗 [Read the full article](https://ift.tt/B07yqRT) ============================= 🔎 Related Phrases: #databricks #bigdata #dataengineering #machinelearning #sql #cloudcomputing #dataanalytics #ai #azure #googlecloud #aws #etl #python #data #database #datawarehouse via Hubert Dudek https://www.youtube.com/channel/UCR99H9eib5MOHEhapg4kkaQ April 22, 2025 at 02:17AM

[hanasatoblogs]

Snowflake vs Redshift vs BigQuery vs Databricks: A Detailed Comparison

In the world of cloud-based data warehousing and analytics, organizations are increasingly relying on advanced platforms to manage their massive datasets. Four of the most popular options available today are Snowflake, Amazon Redshift, Google BigQuery, and Databricks. Each offers unique features, benefits, and challenges for different types of organizations, depending on their size, industry, and data needs. In this article, we will explore these platforms in detail, comparing their performance, scalability, ease of use, and specific use cases to help you make an informed decision.

What Are Snowflake, Redshift, BigQuery, and Databricks?

Snowflake: A cloud-based data warehousing platform known for its unique architecture that separates storage from compute. It’s designed for high performance and ease of use, offering scalability without complex infrastructure management.

Amazon Redshift: Amazon’s managed data warehouse service that allows users to run complex queries on massive datasets. Redshift integrates tightly with AWS services and is optimized for speed and efficiency in the AWS ecosystem.

Google BigQuery: A fully managed and serverless data warehouse provided by Google Cloud. BigQuery is known for its scalable performance and cost-effectiveness, especially for large, analytic workloads that require SQL-based queries.

Databricks: More than just a data warehouse, Databricks is a unified data analytics platform built on Apache Spark. It focuses on big data processing and machine learning workflows, providing an environment for collaborative data science and engineering teams.

Snowflake Overview

Snowflake is built for cloud environments and uses a hybrid architecture that separates compute, storage, and services. This unique architecture allows for efficient scaling and the ability to run independent workloads simultaneously, making it an excellent choice for enterprises that need flexibility and high performance without managing infrastructure.

Key Features:

Data Sharing: Snowflake’s data sharing capabilities allow users to share data across different organizations without the need for data movement or transformation.

Zero Management: Snowflake handles most administrative tasks, such as scaling, optimization, and tuning, so teams can focus on analyzing data.

Multi-Cloud Support: Snowflake runs on AWS, Google Cloud, and Azure, giving users flexibility in choosing their cloud provider.

Real-World Use Case:

A global retail company uses Snowflake to aggregate sales data from various regions, optimizing its supply chain and inventory management processes. By leveraging Snowflake’s data sharing capabilities, the company shares real-time sales data with external partners, improving forecasting accuracy.

Amazon Redshift Overview

Amazon Redshift is a fully managed, petabyte-scale data warehouse solution in the cloud. It is optimized for high-performance querying and is closely integrated with other AWS services, such as S3, making it a top choice for organizations that already use the AWS ecosystem.

Key Features:

Columnar Storage: Redshift stores data in a columnar format, which makes querying large datasets more efficient by minimizing disk I/O.

Integration with AWS: Redshift works seamlessly with other AWS services, such as Amazon S3, Amazon EMR, and AWS Glue, to provide a comprehensive solution for data management.

Concurrency Scaling: Redshift automatically adds additional resources when needed to handle large numbers of concurrent queries.

Real-World Use Case:

A financial services company leverages Redshift for data analysis and reporting, analyzing millions of transactions daily. By integrating Redshift with AWS Glue, the company has built an automated ETL pipeline that loads new transaction data from Amazon S3 for analysis in near-real-time.

Google BigQuery Overview

BigQuery is a fully managed, serverless data warehouse that excels in handling large-scale, complex data analysis workloads. It allows users to run SQL queries on massive datasets without worrying about the underlying infrastructure. BigQuery is particularly known for its cost efficiency, as it charges based on the amount of data processed rather than the resources used.

Key Features:

Serverless Architecture: BigQuery automatically handles all infrastructure management, allowing users to focus purely on querying and analyzing data.

Real-Time Analytics: It supports real-time analytics, enabling businesses to make data-driven decisions quickly.

Cost Efficiency: With its pay-per-query model, BigQuery is highly cost-effective, especially for organizations with varying data processing needs.

Real-World Use Case:

A digital marketing agency uses BigQuery to analyze massive amounts of user behavior data from its advertising campaigns. By integrating BigQuery with Google Analytics and Google Ads, the agency is able to optimize its ad spend and refine targeting strategies.

Databricks Overview

Databricks is a unified analytics platform built on Apache Spark, making it ideal for data engineering, data science, and machine learning workflows. Unlike traditional data warehouses, Databricks combines data lakes, warehouses, and machine learning into a single platform, making it suitable for advanced analytics.

Key Features:

Unified Analytics Platform: Databricks combines data engineering, data science, and machine learning workflows into a single platform.

Built on Apache Spark: Databricks provides a fast, scalable environment for big data processing using Spark’s distributed computing capabilities.

Collaboration: Databricks provides collaborative notebooks that allow data scientists, analysts, and engineers to work together on the same project.

Real-World Use Case:

A healthcare provider uses Databricks to process patient data in real-time and apply machine learning models to predict patient outcomes. The platform enables collaboration between data scientists and engineers, allowing the team to deploy predictive models that improve patient care.

People Also Ask

1. Which is better for data warehousing: Snowflake or Redshift?

Both Snowflake and Redshift are excellent for data warehousing, but the best option depends on your existing ecosystem. Snowflake’s multi-cloud support and unique architecture make it a better choice for enterprises that need flexibility and easy scaling. Redshift, however, is ideal for organizations already using AWS, as it integrates seamlessly with AWS services.

2. Can BigQuery handle real-time data?

Yes, BigQuery is capable of handling real-time data through its streaming API. This makes it an excellent choice for organizations that need to analyze data as it’s generated, such as in IoT or e-commerce environments where real-time decision-making is critical.

3. What is the primary difference between Databricks and Snowflake?

Databricks is a unified platform for data engineering, data science, and machine learning, focusing on big data processing using Apache Spark. Snowflake, on the other hand, is a cloud data warehouse optimized for SQL-based analytics. If your organization requires machine learning workflows and big data processing, Databricks may be the better option.

Conclusion

When choosing between Snowflake, Redshift, BigQuery, and Databricks, it's essential to consider the specific needs of your organization. Snowflake is a flexible, high-performance cloud data warehouse, making it ideal for enterprises that need a multi-cloud solution. Redshift, best suited for those already invested in the AWS ecosystem, offers strong performance for large datasets. BigQuery excels in cost-effective, serverless analytics, particularly in the Google Cloud environment. Databricks shines for companies focused on big data processing, machine learning, and collaborative data science workflows.

The future of data analytics and warehousing will likely see further integration of AI and machine learning capabilities, with platforms like Databricks leading the way in this area. However, the best choice for your organization depends on your existing infrastructure, budget, and long-term data strategy.

[tudipblog]

Medallion Architecture: A Scalable Framework for Modern Data Management

In the current big data era, companies must effectively manage data to make data-driven decisions. One such well-known data management architecture is the Medallion Architecture. This architecture offers a structured, scalable, modular approach to building data pipelines, ensuring data quality, and optimizing data operations.

What is Medallion Architecture?

Medallion Architecture is a system for managing and organizing data in stages. Each stage, or “medallion,” improves the quality and usefulness of the data, step by step. The main goal is to transform raw data into meaningful data that is ready for the analysis team.

The Three Layers of Medallion Architecture:

Bronze Layer (Raw Data):This layer stores all raw data exactly as it comes in without any changes or cleaning, preserving a copy of the original data for fixing errors or reprocessing when needed. Example: Logs from a website, sensor data, or files uploaded by users.

Silver Layer (Cleaned and Transformed Data):The Silver Layer involves cleaning, organizing, and validating data by fixing errors such as duplicates or missing values, ensuring the data is consistent and reliable for analysis, such as removing duplicate customer records or standardizing dates in a database Example: Removing duplicate customer records or standardizing dates in a database.

Gold Layer (Business-Ready Data):The Gold Layer contains final, polished data optimized for reports, dashboards, and decision-making, providing businesses with exactly the information they need to make informed decisions Example: A table showing the total monthly sales for each region

Advantages:

Improved Data Quality: Incremental layers progressively refine data quality from raw to business-ready datasets

Scalability: Each layer can be scaled independently based on specific business requirements

Security: If you have a large team to handle, you can separate them by their level

Modularity: The layered approach separates responsibilities, simplifying management and debugging

Traceability: Raw data preserved in the Bronze layer ensures traceability and allows reprocessing when issues arise in downstream layers

Adaptability: The architecture supports diverse data sources and formats, making it suitable for various business needs

Challenges:

Takes Time: Processing through multiple layers can delay results

Storage Costs: Storing raw and processed data requires more space

Requires Skills: Implementing this architecture requires skilled data engineers familiar with ETL/ELT tools, cloud platforms, and distributed systems

Best Practices for Medallion Architecture:

Automate ETL/ELT Processes: Use orchestration tools like Apache Airflow or AWS Step Functions to automate workflows between layers

Enforce Data Quality at Each Layer: Validate schemas, apply deduplication rules, and ensure data consistency as it transitions through layers

Monitor and Optimize Performance: Use monitoring tools to track pipeline performance and optimize transformations for scalability

Leverage Modern Tools: Adopt cloud-native technologies like Databricks, Delta Lake, or Snowflake to simplify the implementation

Plan for Governance: Implement robust data governance policies, including access control, data cataloging, and audit trails

Conclusion

Medallion Architecture is a robust framework for building reliable, scalable, and modular data pipelines. Its layered approach allows businesses to extract maximum value from their data by ensuring quality and consistency at every stage. While it comes with its challenges, the benefits of adopting Medallion Architecture often outweigh the drawbacks, making it a cornerstone for modern data engineering practices.

To learn more about this blog, please click on the link below: https://tudip.com/blog-post/medallion-architecture/.

[praveennareshit]

🚀 Master Azure Data Engineering – Free Online Master Class

Want to become an Azure Data Engineer or ETL Developer? Join this free workshop led by Mr. Bhaskar, covering everything from Azure Data Factory to Big Data pipelines.

📅 Date: 17th April 2025 🕕 Time: 6:00 PM IST 🏫 Mode: Classroom & Online 🔗 Register: https://tr.ee/9JZIC5

🔍 What You’ll Learn:

Azure Architecture & Core Services

Building Robust ETL Pipelines

Azure Data Lake, Synapse, and Databricks

Real-time Projects

Interview Prep & Certification Guidance

🎓 Ideal for beginners & cloud career switchers.

Explore more batches: https://linktr.ee/NIT_Training

[lxclusive]

🧮🛠📊✒️🗝📇

DataCon Sofia 10.04.2025!

#SchwarzIT #DataCon #databricks #stackit #Europe #Bulgaria

AI, Gen AI, ML, Super Brain, Agents, Lakehouses, Backbones, Workflows...

It is all about the data:

Where? What? When? Why? Who? How?

Thank you for having us at the event!