#Databricks Lakehouse

[gaininfotech]

Why Can We Say Databricks Lakehouse Problem Solving Tool?

Databricks lakehouse would definitely help you in this regard since it enables you to do data processing, transformation, and analysis. Due to this convenience, you stay ahead by addressing challenges corresponding to data processing and analytics. For more information call us @ 9971900416 or mail us at info@netkingtechnologies.com

For more details - https://gaininfotech.com/blog/2023/10/30/why-can-we-say-databricks-lakehouse-problem-solving-tool/

[rajaniesh]

Real-World Application of Data Mesh with Databricks Lakehouse

Explore how a global reinsurance leader transformed its data systems with Data Mesh and Databricks Lakehouse for better operations and decision-making.

[b2bcybersecurity]

Ein Spezialist für datenzentrierte Cybersicherheit, baut die Abdeckung seiner Datensicherheitsplattform auf Databricks aus. Auf diese Weise können auch kritische Daten der Data-Intelligence-Plattform kontinuierlich identifiziert und klassifiziert, Gefährdungen beseitigt sowie Bedrohungen erkannt und gestoppt werden. Tausende Unternehmen weltweit setzten auf Databricks bei der Erstellung, Bereitstellung, gemeinsamen Nutzung und Wartung von Daten, Analysen und KI-Lösungen. Cloud-Plattformen wie Databricks bieten zwar eine hohe Leistung und Flexibilität, erhöhen aber auch das Risiko, wenn die Datenmenge wächst. Deshalb ist ein proaktiver, datenzentrierter Ansatz erforderlich, der erkennt, wo sich sensitive Daten befinden, und in der Lage ist, Risiken auch in großem Umfang zu beseitigen. Mit Varonis können Unternehmen die Sicherheit ihrer Cloud-Daten kontinuierlich verbessern. Dafür bietet Varonis für Databricks umfangreiche Funktionen: - Automatische Identifizierung gefährdeter Daten: Varonis erkennt und klassifiziert sensitive Daten im gesamten Data Lakehouse – etwa in Workspaces, Datenbanken und Schemas. Varonis bietet dabei einen umfassenden Überblick über Benutzer- und Gruppenzugriffsberechtigungen und Identitäten in Databricks. - Proaktive Abhilfe: Die Identifizierung von Datenrisiken ist nur ein Teil der Herausforderung. Sicherheitsverantwortliche müssen auch Probleme schnell beheben und Schwachstellen schließen, bevor sie ausgenutzt werden können. Varonis unterstützt Unternehmen dabei, Probleme proaktiv zu beheben und Risiken zu reduzieren. - Umfassende Bedrohungserkennung: Die aktive Erkennung von Sicherheitsrisiken in der Cloud ist von entscheidender Bedeutung, insbesondere wenn Angreifer mit legitimen Anmeldedaten eindringen. Varonis bietet eine umfassende Abdeckung der gesamten Kill Chain, damit Bedrohungen für die Daten schnell erkannt und beseitigt werden können. „Durch das Modell der geteilten Verantwortung sind die IT- und Sicherheitsteams und nicht der Anbieter für den Schutz der Unternehmensdaten verantwortlich“, erklärt Volker Sommer, Regional Sales Director DACH von Varonis. „Durch die stetig wachsende Menge an Daten in Cloud-Data-Warehouses und Lakehouses steigen auch die Risiken. Diese lassen sich nur durch eine tiefe Transparenz und hohe Automatisierung wirkungsvoll minimieren. Dies gilt umso mehr, da Angreifer in aller Regel nicht mehr einbrechen, sondern sich mit gestohlenen oder ergaunerten Benutzerinformationen einloggen.“     Passende Artikel zum Thema   Read the full article

[cleverhottubmiracle]

[ad_1] Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More German software giant SAP is pushing the bar on the data front to power next-gen AI use cases. The company today introduced Business Data Cloud (BDC), a new SaaS product that embraces lakehouse architecture to help teams enrich their SAP ecosystem data with external data assets from different source systems and drive long-term value. The product is the outcome of a landmark collaboration with data ecosystem major Databricks. Essentially, SAP BDC natively integrates capabilities and data from Databricks’ data intelligence platform. This removes the need for creating and maintaining complex pipelines and creates a harmonized data foundation for advanced AI agents and analytical workloads. Several enterprises, including Henkel, are using BDC to power their AI projects. SAP itself is using the enriched BDC to power a new era of Joule agents focused on specific domains like finance, service and sales. The development makes SAP another notable player, much like Microsoft and Salesforce, bolstering its data platform to lay down the foundation for AI. SAP’s revamped data foundation Over the years, SAP has established itself as one of the leading players in enterprise resource planning (ERP) with S4/HANA cloud and several mission-critical applications for finance, supply chain and human capital management. These apps produce petabyte-scale data with business context and have been powering AI and analytical value for teams, via the company’s business technology platform (BTP).  So far, SAP BTP has had a ‘datasphere’ that allows enterprises to connect data from SAP with information from non-SAP systems and eventually link it with SAP analytics cloud and other internal tools for downstream applications. Now, the company is evolving this experience into the unified BDC, natively powered by Databricks. What SAP business data cloud has on offer What this means is that SAP is embracing lakehouse architecture, creating a unified foundation that combines all SAP data products — from finance, spend and supply chain data in SAP S/4HANA and SAP Ariba, to learning and talent data in SAP SuccessFactors — with structured and unstructured data from other varied yet business-critical systems, stored in Databricks. Once the data is unified (via zero-copy, bi-directional sharing), SAP BDC can leverage Databricks-specific capabilities for workloads like data warehousing, data engineering and AI, all governed by Databricks unity catalog. “We take all of these different data products, which are provisioned and managed by SAP…and we will persist them into the lakehouse of SAP business data cloud, in a harmonized data model,” Irfan Khan, president and CPO for SAP data and analytics, told VentureBeat. “This lakehouse will have Databricks capabilities for users to build upon.” Previously, said Khan, users who had a large percentage of their data in Databricks and SAP data in S4 or BW had to build and manage complex pipelines and replicate all the data assets to the SAP platform while rebuilding the entire semantics and the core data model at the same time. The approach took time and required them to keep their pipelines updated with changing data. However, with Databricks’ native integration, users have access to everything in one place and can directly do data engineering, data science and other tasks on top of the BDC. “In Datasphere, you had a means of doing a similar thing, but they were all customer-managed data products,” Khan explained. “So, you had to go into the data platform, select the data sources and build the data pipelines. Then, you had to figure out what to replicate. Here, it’s all managed by SAP.” What this means for enterprises At its core, this Databricks-powered product gives teams a faster, simpler way to unify and mobilize their business data assets locked within SAP and Databricks environments.  The combined, semantically-enhanced data will pave the way for building next-gen AI applications aimed at different use cases. For instance, a team could use Databricks’ Mosaic AI capabilities to develop domain-specific AI agents that could use context from SAP’s business data as well as external Databricks-specific data to automate certain human capital management or supply chain functions.  Notably, SAP itself is tapping this enhanced data foundation to power ready-to-use Joule agents aimed at automating tasks and accelerating workflows across sales, service and finance functions. These agents deeply understand end-to-end processes and collaborate to solve complex business problems. Beyond this, BDC will have an “insight apps” capability, which will allow users to connect their data products and AI models with external real-time data to deliver advanced analytics and planning across business functions. More data partners to come While the partnership underscores a big move for both Databricks and SAP, it is important to note that the Ali Ghodsi-led data major won’t be the only one bolstering BDC.  According to Khan, data sharing and ecosystem openness are the company’s first design principles — and they will expand to other data platforms through their partner connect capabilities. This means an enterprise user will be able to choose the platform they prefer (or that they are locked into) and bi-directionally share data for targeted use cases. Daily insights on business use cases with VB Daily If you want to impress your boss, VB Daily has you covered. We give you the inside scoop on what companies are doing with generative AI, from regulatory shifts to practical deployments, so you can share insights for maximum ROI. Read our Privacy Policy Thanks for subscribing. Check out more VB newsletters here. An error occured. [ad_2] Source link

[qubixo1]

SAP TAPS DATABRICK to enhance the willingness of artificial intelligence with the new business cloud

Join daily and weekly newsletters to obtain the latest updates and exclusive content to cover the leading artificial intelligence in the industry. Learn more German software giant Bait The tape is pushed on the front of the data to run cases of artificial intelligence from the next generation. The company today foot Business Data Cloud (BDC), the new Saas product that embraces Lakehouse’s…

[rajaniesh]

Scaling Your Data Mesh Architecture for maximum efficiency and interoperability

[jack5980]

Databrick consulting services

Discover the transformative potential of Databricks with Xorbix Technologies, a leading Databricks consulting services provider. From AI and machine learning to data modernization and cloud migration, our certified Databricks engineers specialize in delivering custom solutions tailored to your unique business needs. Partner with us to leverage the Databricks Lakehouse Platform, Genie, and AutoML for streamlined analytics, seamless data governance, and actionable insights. Let us be your Databricks service provider company of choice!

[datayuan]

两个甲骨文的“叛徒”，砸烂了数仓的旧世界

为什么Snowflake能够击败AWS Redshift、Google BigQuery这些“出道即王炸”的玩家？它的技术和策略到底有多高明？而面对Databricks以Lakehouse杀入市场的挑战，Snowflake的护城河是否足够坚固？后来，Snowflake又为什么有一段时间被资本所“抛弃”？

[kadellabs69]

Enhancing Data Management and Analytics with Kadel Labs: Leveraging Databricks Lakehouse Platform and Databricks Unity Catalog

In today’s data-driven world, companies across industries are continually seeking advanced solutions to streamline data processing and ensure data accuracy. Data accessibility, security, and analysis have become key priorities for organizations aiming to harness the power of data for strategic decision-making. Kadel Labs, a forward-thinking technology solutions provider, has recognized the importance of robust data solutions in helping businesses thrive. Among the most promising tools they employ are the Databricks Lakehouse Platform and Databricks Unity Catalog, which offer scalable, secure, and versatile solutions for managing and analyzing vast amounts of data.

[cert007]

Databricks Certified Data Engineer Professional Practice Exam For Best Preparation

Are you aspiring to become a certified data engineer with Databricks? Passing the Databricks Certified Data Engineer Professional exam is a significant step in proving your advanced data engineering skills. To simplify your preparation, the latest Databricks Certified Data Engineer Professional Practice Exam from Cert007 is an invaluable resource. Designed to mimic the real exam, it provides comprehensive practice questions that will help you master the topics and build confidence. With Cert007’s reliable preparation material, you can approach the exam with ease and increase your chances of success.

Overview of the Databricks Certified Data Engineer Professional Exam

The Databricks Certified Data Engineer Professional exam evaluates your ability to leverage the Databricks platform for advanced data engineering tasks. You will be tested on a range of skills, including:

Utilizing Apache Spark, Delta Lake, and MLflow to manage and process large datasets.

Building and optimizing ETL pipelines.

Applying data modeling principles to structure data in a Lakehouse architecture.

Using developer tools such as the Databricks CLI and REST API.

Ensuring data pipeline security, reliability, and performance through monitoring, testing, and governance.

Successful candidates will demonstrate a solid understanding of Databricks tools and the capability to design secure, efficient, and robust pipelines for data engineering.

Exam Details

Number of Questions: 60 multiple-choice questions

Duration: 120 minutes

Cost: $200 per attempt

Primary Coding Language: Python (Delta Lake functionality references are in SQL)

Certification Validity: 2 years from the date of passing

Exam Objectives and Weightage

The exam content is divided into six key objectives:

Databricks Tooling (20%) Proficiency in Databricks developer tools, including the CLI, REST API, and notebooks.

Data Processing (30%) Deep understanding of data transformation, optimization, and real-time streaming tasks using Databricks.

Data Modeling (20%) Knowledge of structuring data effectively for analysis and reporting in a Lakehouse architecture.

Security and Governance (10%) Implementation of secure practices for managing data access, encryption, and auditing.

Monitoring and Logging (10%) Ability to use tools and techniques to monitor pipeline performance and troubleshoot issues.

Testing and Deployment (10%) Knowledge of building, testing, and deploying reliable data engineering solutions.

Preparation Tips for Databricks Certified Data Engineer Professional Exam

1. Leverage Cert007 Practice Exams

The Databricks Certified Data Engineer Professional Practice Exam by Cert007 is tailored to provide a hands-on simulation of the real exam. Practicing with these questions will sharpen your understanding of the key concepts and help you identify areas where additional study is needed.

2. Understand the Databricks Ecosystem

Develop a strong understanding of the core components of the Databricks platform, including Apache Spark, Delta Lake, and MLflow. Focus on how these tools integrate to create seamless data engineering workflows.

3. Study the Official Databricks Learning Pathway

Follow the official Data Engineer learning pathway provided by Databricks. This pathway offers structured courses and materials designed to prepare candidates for the certification exam.

4. Hands-On Practice

Set up your own Databricks environment and practice creating ETL pipelines, managing data in Delta Lake, and deploying models with MLflow. This hands-on experience will enhance your skills and reinforce theoretical knowledge.

5. Review Security and Governance Best Practices

Pay attention to secure data practices, including access control, encryption, and compliance requirements. Understanding governance within the Databricks platform is essential for this exam.

6. Time Management for the Exam

Since you’ll have 120 minutes to answer 60 questions, practice pacing yourself during the exam. Aim to spend no more than 2 minutes per question, leaving time to review your answers.

Conclusion

Becoming a Databricks Certified Data Engineer Professional validates your expertise in advanced data engineering using the Databricks platform. By leveraging high-quality resources like the Cert007 practice exams and committing to hands-on practice, you can confidently approach the exam and achieve certification. Remember to stay consistent with your preparation and focus on mastering the six key objectives to ensure your success.

Good luck on your journey to becoming a certified data engineering professional!

[otiskeene]

Top 5 Big Data Tools Of 2023

In today’s data-rich environment, big data encompasses vast amounts of structured, semi-structured, and unstructured data. This data can fuel Machine Learning, predictive modeling, and various analytics projects, bringing insights that drive better decisions. #BigDataImpact

Big Data Tools are the key to unlocking the potential of this information, helping businesses process, analyze, and visualize data to uncover trends and insights. With so many options available, choosing the best tool for your needs is essential.

This guide presents the Top 5 Big Data Tools of 2023, giving you an overview of each to help you make the best choice.

Top 5 Big Data Tools of 2023

1. Apache Hadoop

Apache Hadoop, a product of the Apache Software Foundation, is an industry favorite, used by companies like AWS and IBM. Known for its scalability and efficiency, Hadoop uses HDFS for data storage and MapReduce for data processing, allowing businesses to handle large data sets across various formats.

2. Databricks Lakehouse Platform

Databricks Lakehouse, trusted by top companies like H&M and Nationwide, combines the best of data lakes and warehouses. By unifying data and eliminating silos, Databricks enables faster analytics, better collaboration, and more efficient data management.

3. Qubole

Qubole provides comprehensive data lake services, offering a cost-effective solution for managing large datasets. With support from brands like Disney and Adobe, Qubole’s open platform offers flexibility and fast data processing, making it a top choice for data scientists and engineers.

4. Sisense

Sisense bridges the gap between data analysis and visualization, offering a drag-and-drop dashboard, built-in ETL, and comprehensive data tools. It’s user-friendly, making it perfect for business users who need insights without requiring technical expertise.

5. Talend

Talend is a powerful data integration and management tool, offering end-to-end solutions that support a variety of data architectures. Known for its open-source offerings and customization, Talend is ideal for organizations looking for a scalable, reliable data solution.

Final Thoughts

Choosing the right Big Data Tool allows businesses to transform complex datasets into valuable insights. Equip yourself with one of these top tools to leverage the full power of big data!

[thedatagroupnewsservice]

Ensuring Quality Forecasts with Databricks Lakehouse Monitoring http://dlvr.it/T9mjbY

[dataplatr-1]

Dataplatr: Your Databricks Consulting Partner for AI, Data Analytics, and Data Engineering

Businesses need powerful tools and expert guidance to stay ahead of the curve. This is where Dataplatr, a trusted Databricks consulting partner, steps in to empower organizations with cutting-edge solutions in AI, data analytics, and data engineering.

Why Choose Dataplatr as Your Databricks Consulting Partner?

At Dataplatr, we pride ourselves on being a certified Databricks consulting partner, delivering Customized solutions to meet your unique business needs. By Using the Databricks Lakehouse Platform, we enable organizations to harness the power of unified data for better decision-making, faster innovation, and seamless collaboration.

Our team of experts is proficient in working with Databricks’ advanced tools and technologies to provide:

Scalable AI Solutions: Empower your business with AI-driven insights and predictive models, tailored to your industry.

Advanced Data Analytics: Unlock actionable insights from vast amounts of data using the power of Databricks and our expertise.

Efficient Data Engineering: Streamline your data pipelines and processes to ensure smooth, real-time data flow across your organization.

Our Expertise as a Databricks Consulting Partner

Dataplatr understands that each organization’s data journey is unique. As a Databricks partner, we focus on delivering customized solutions to help you achieve your business goals. Whether it's optimizing cloud data infrastructure, enabling real-time analytics, or scaling AI and machine learning capabilities, we are equipped to make it happen.

How Dataplatr Supports Your Digital Transformation Journey

Digital transformation is no longer optional, it's essential. With Dataplatr as your Databricks consulting partner, we help businesses integrate Databricks into their digital transformation strategies. Our services focus on scalability, innovation, and delivering measurable ROI.

Accelerating AI Adoption with Dataplatr's Databricks Expertise

Artificial Intelligence (AI) is a key driver of competitive advantage, and Dataplatr helps organizations adopt AI solutions with ease. We Databricks consulting partner, we provide end-to-end AI support, from the development of machine learning models to their deployment on the Databricks platform. Our experts ensure that your AI initiatives are tailored to meet your specific business needs, helping you drive innovation and gain a deep understanding of your data.

Dataplatr + Databricks: Driving Innovation Together

As a leading Databricks consulting partner, Dataplatr collaborates closely with businesses to unleash the full potential of their data. By combining our deep knowledge of Databricks with a commitment to excellence, we enable organizations to drive innovation, improve operational efficiency, and achieve measurable business outcomes.

[rajaniesh]

Implementing Data Mesh on Databricks: Harmonized and Hub & Spoke Approaches

Explore the Harmonized and Hub & Spoke Data Mesh models on Databricks. Enhance data management with autonomous yet integrated domains and central governance. Perfect for diverse organizational needs and scalable solutions. #DataMesh #Databricks