How to Become a Data Engineer — II
… using Databricks & Other Resources
Part II — Expand Your Skills
- Part I: The Fundamentals
- Part III: Stay Up To Date
Last Update 2024–09–03: additional section about ‘Semantic Layers’
- Data engineers are vital for business success as they organize and analyze data, collaborate with stakeholders, and support strategic goals through data engineering practices.
- Enhancing expertise and refining practical skills are critical to optimizing data management efficiency and operational effectiveness in modern data-driven environments.
Table of Contents
· Introduction
· Overview
· Driving Business
· Expand Your Knowledge
∘ Stream Processing
∘ Data Warehousing
∘ Reverse ETL
∘ Data Sharing
∘ Data Governance
∘ Data Modeling
∘ Semantic Layer
∘ Regulatory Compliance
· Deepen Your Practice
∘ Databricks Solution Accelerators
∘ Databricks Brickbuilder Program
· Databricks Admin Essentials
· Conclusion
Introduction
In today’s data-driven landscape, data engineers play a critical role in business success by turning data into actionable insights. The ability to collaborate is essential to align data work with strategic business goals.
To strengthen their role, data engineers need to continuously expand their expertise. Understanding stream processing and mastering areas such as data warehousing, reverse ETL, data sharing and data governance are essential for effective data management and innovation. Expert knowledge of regulatory compliance ensures data integrity and mitigates risk.
Deepening practice involves applying new skills and knowledge, utilizing resources such as Databricks Solution Accelerators and Brickbuilder Solutions. Proficiency in Databricks administration ensures efficient use of resources, secure data access and controlled cloud costs that optimize operations and enable effective data management.
Overview
Driving Business: Data engineers are essential to business success because they organize and analyze data to gain actionable insights. Collaboration skills, including effective communication and understanding of stakeholders, are critical. Data engineering complements data science and supports strategic goals. Partnering with marketers improves campaigns and customer experiences. Investing in data engineering is essential for informed decision-making, sustainable growth and innovation in the data-driven landscape.
Expand Your Knowledge: To empower their critical role in the organization, data engineers should continuously expand their expertise. Understanding stream processing is crucial for real-time data analysis, aiding decision-making and operational efficiency. Engineers need proficiency in setting up and managing streaming pipelines. Knowledge of data warehousing, reverse ETL, data sharing, governance, and compliance is essential for effective data management and innovation. Regulatory compliance expertise ensures adherence to legal standards, mitigating risks and upholding data integrity.
Deepen Your Practice: The constant expansion of their expertise requires data engineers to apply new knowledge and skills in practice. Databricks Solution Accelerators and Brickbuilder Solutions are key resources for data engineers. With curated tools and guidelines, Solution Accelerators streamline implementation, while Brickbuilder Solutions address diverse industry needs on the Databricks Lakehouse platform. In addition, Brickbuilder Accelerators provide an efficient solution to critical analytical challenges, reducing costs and increasing productivity in a short period of time.
Databricks Admin Essentials: Databricks administration skills are essential for regular data engineers to ensure efficient use of resources, secure data access and controlled cloud costs, ultimately optimizing operations and enabling effective data management. Learn Databricks administration basics, including workspace organization, disaster recovery, and cost management, with emphasis on key roles and features like access control, Unity Catalog for data management, and cost-effective strategies.
Driving Business
Data engineers are a key enabler of business success due to their role in organizing, processing and analyzing large amounts of data. By building a robust data infrastructure and implementing efficient data pipelines, they enable organizations to gain actionable insights and make informed decisions. Their expertise empowers organizations to use data effectively, innovate and stay competitive in today’s data-driven landscape.
Mastering Collaboration and Communication as a Data Engineer
Learn about the essential role of teamwork for data engineers in this comprehensive guide. Discover the importance of effective collaboration and communication to project success, from understanding stakeholder needs to fostering a positive team environment. Explore strategies for healthy code review cycles, enforcing boundaries and celebrating successes. With practical insights and expert advice, you’ll master the art of teamwork and increase your impact as a data engineer.
Remember that being a team player not only makes you a great asset to your organization, but it also assists you in growing as a professional and adds to a positive working atmosphere!
Dear Data Engineer — Get to know your Stakeholders
Understanding stakeholders is critical for data engineers to align technical solutions with business goals. By understanding stakeholders’ goals, challenges and workflows, data engineers can tailor solutions to their needs, increasing relevance and effectiveness. In addition, this insight enables the anticipation of needs, proactively identifying opportunities for improvement and providing actionable insights for strategic decision making. This understanding fosters collaboration, builds trust and makes data engineers key partners in driving business success through data-driven initiatives.
Understanding the work of stakeholders is paramount for data engineers as it promotes alignment between technical solutions and business goals.
Data science and data engineering (should) go together like peas and carrots
Data science and data engineering are often confused, but they are distinct and complementary disciplines that are critical to a company’s success. Data science extracts insights from data, while data engineering ensures that the data infrastructure supports strategic goals. Collaboration between data scientists and data engineers is essential for reliable, actionable insights. Without the right technical support, data scientists can struggle, leading to inefficiencies and failed projects. Investing in both disciplines and fostering collaboration is critical to unlocking the full potential of data and securing your organization’s future in an increasingly data-driven world.
You can benefit from investing in both data science and data engineering, then ensuring they work together by default and as effectively as possible.
How Can Data Engineering & Marketing Be Used Together?
Data engineering improves marketing strategies by providing insights and optimizing processes for personalized campaigns and customer experiences. Marketers use data for segmentation and analysis to make data-driven decisions. Overcoming challenges such as data quality and talent shortages is critical. Effective strategies and collaboration between data engineering and marketing drive success. Completing certification courses in data engineering provides a competitive advantage in the job market.
Data engineering is an invaluable and influential tool in modern marketing.
Data Engineering for Data-Driven Marketing
In the data-driven marketing landscape, data engineers and marketers work closely together for business success. The role of data engineers in this partnership, explored here, is often underestimated. Emphasizing collaboration and business context can amplify their impact. Aligning data processing with strategic goals and standardizing data requirements are critical. New data processing tools streamline analysis and provide valuable insights that enable marketers to create personalized campaigns and improve customer engagement in the digital age.
With data as a driving force within marketing, the need to apply all available data becomes paramount.
Data Engineers: The Hidden Drivers of the Great Data Disruption
Data engineers play a critical but often overlooked role in addressing the “Great Data Disruption”. As data becomes an increasingly integral part of business operations, these professionals are at the forefront, developing systems to capture, store and analyze data assets. Data technology is evolving rapidly and new technologies are constantly emerging. To stay competitive, companies need to adapt and help their data engineers acquire different skills. It is important for IT managers to encourage innovation, explore new tools and create a culture of continuous learning. Those who fail to evolve risk losing talent and market share in an increasingly data-driven world.
Rather than reacting to, or trying to keep pace with, the “Great Data Disruption,” smart leaders will partner with data engineers to capitalize on this moment in time.
Expand Your Knowledge
Expanding knowledge is essential for data engineers to keep up with evolving technologies and trends, adapt to changing data landscapes, learn new tools and refine problem-solving skills. Continuous learning helps them stay competitive, provide innovative solutions and effectively utilize new technologies for business success.
Stream Processing
Processing data streams is crucial for real-time data analysis, as it enables companies to gain insights and make timely decisions. Processing continuous streams of data enables rapid response to changing conditions, increases operational efficiency and creates a personalized customer experience. Stream processing also supports various use cases such as fraud detection, predictive maintenance and dynamic pricing to help organizations stay competitive in today’s fast-paced and data-driven environment.
Data Streaming
The Databricks Data Intelligence Platform simplifies real-time analytics, ML and applications and enables data teams to create streaming data workloads using familiar languages and tools. It automates production aspects, simplifying development and operations while unifying governance across clouds. With Delta Live Tables for data ingestion and ETL, the Spark Structured Streaming Engine and automated operational tools, organizations can efficiently deploy and manage streaming pipelines, ensuring real-time insights and improved productivity across the board.
Structured Streaming with Spark. Ask Databricks, round four!
Structured streaming in Apache Spark simplifies real-time data processing, blurs the lines between batch processing and streaming, and responds to the demand for efficient stream processing. Stream-to-stream joins rely on watermarks for synchronization to balance latency and accuracy. Delta Live Tables from Databricks extends Spark Structured Streaming with a declarative approach that simplifies infrastructure tasks. Monitoring and debugging tools such as event logs and Spark UI support application observability. While “for each batch” processing is suitable for specific use cases, the choice between streaming and batch processing depends on the priorities and goals of the workload.
Udemy: Apache Spark and Databricks — Stream Processing in Lakehouse
The high-rated Udemy course of Prashant Kumar Pandey covers various real-time stream processing concepts, including Spark Structured Streaming APIs and architecture. It delves into working with streaming sources and sinks, with a focus on Kafka for data engineers. Participants learn about state-less and stateful streaming transformations, windowing aggregates, watermarking, and state cleanup. The curriculum also includes streaming joins and aggregation, along with strategies for handling memory problems. Additionally, participants gain practical experience working with Azure Databricks and complete a capstone project involving the development of a streaming application within a Lakehouse architecture.
It’s Time for Streaming Architectures for Every Use Case
This resource explores the transition from batch to streaming processing, revisits common use cases and highlights the benefits of Spark Structured Streaming. It emphasizes the scalability, simplicity and cost-effectiveness of Databricks when implementing streaming architectures for real-time data processing. With Databricks, organizations gain flexibility in adapting latency, cost and throughput requirements, ensuring readiness for evolving data needs in today’s dynamic landscape.
Data Warehousing
Data warehousing is important for data engineers because traditional BI and analytics workflows are evolving and often require data duplication between data lakes and warehouses. The lakehouse concept unifies these functions, simplifies architectures and reduces costs. With tools like Databricks SQL, engineers can integrate warehousing with data lakes, enabling flexible, high-performance multi-cloud architectures. Case studies underline the success of the concept and make it indispensable for modern data engineering.
Big Book of Data Warehousing and BI
Delve into data warehousing with the Data Intelligence Platform using practical examples from the areas of governance, analytics and AI. As data complexity increases, a practical guide is essential. Learn how to go from raw data to actionable insights using a lakehouse architecture, covering data collection, analytics and AI. Access reference architectures, best practices and case studies from companies like Akamai and InMobi and gain practical insights into scalable, powerful and cost-effective data warehousing with Databricks Platform.
A guide to Databricks SQL and Data Warehousing talks at Data + AI Summit 2023
Databricks SQL, the serverless data warehouse on Lakehouse, is growing in popularity. At the recent virtual event “The Case for Moving to the Lakehouse”, one could learn more about the features and benefits of the platform and participate in a variety of sessions, tutorials and training on generative AI, data and AI governance, and building systems for improved performance and usability at the Data + AI Summit.
Reverse ETL
Reverse ETL is critical in data engineering as it facilitates the seamless transfer of insights derived from analytics back into operational systems. This ensures that decision makers have access to up-to-date and actionable data, business processes are optimized and business growth is driven by closing the gap between analytics and operations.
What is Reverse ETL? Here’s everything you need to know in 2024
Reverse ETL empowers teams with real-time, actionable insights to improve customer experience and internal processes. This process differs from traditional ETL pipelines as it enables data to be activated for immediate use, driving operational analysis and informed decision-making. Reverse ETL tools streamline data flow and improve efficiency and adaptability in today’s dynamic data landscape.
Reverse ETL is the process of syncing data from a source of truth like a data warehouse or data lake to a system of actions like CRM, advertising platform, or other SaaS app to activate data.
Hightouch Reverse ETL for your Marketing Data.
Reverse ETL, like Hightouch, increases the value of data by extracting and synchronizing warehouse data with downstream tools and provides real-time access for operational teams to take proactive steps. Unlike traditional ETL, it maintains data granularity for timely decisions and personalized experiences, while seamlessly integrating with multiple sources for accurate analysis. Hightouch simplifies data engineering by automating API connections, saving time and costs. Marketing, sales, product and customer support teams benefit from synchronized data that drives business growth and improves the customer experience.
How to Use Hightouch for Reverse ETL with Databricks
Learn how to drive analytics back into your organization with reverse ETL. With Databricks as the centralized analytics hub, Hightouch enables the seamless transfer of transformed data to operational systems like advertising, marketing, and CRM platforms. This process eliminates the need for custom data pipelines and simplifies maintenance, allowing business teams to access and act on valuable insights efficiently. The post outlines steps for syncing data with Hightouch, emphasizing its compliance and integration benefits, and offers resources for getting started with Databricks and Hightouch.
Enabling Operational Analytics on the Databricks Lakehouse Platform With Census Reverse ETL
The blog post introduces Reverse ETL with Census and highlights its role in increasing the value of a data lakehouse by seamlessly synchronizing data from Databricks with various tools used by sales, marketing and operations teams. It bridges the gap between data and operations teams through its key components — models, segments and syncs — while ensuring data security and governance, ultimately empowering data engineers and professionals to make data more valuable and actionable across departments.
Data Sharing
Data sharing is essential for collaboration and innovation in data, analytics and AI. Delta Sharing, introduced by Databricks and the Linux Foundation, provides an open source solution that enables seamless sharing of live datasets, models and more across platforms and geographies. With robust security and governance features built into the Databricks Unity Catalog, Delta Sharing facilitates secure collaboration at scale, simplifies data discovery and improves analytics results.
Databricks — Data Sharing
Databricks and the Linux Foundation have unveiled Delta Sharing, the first open source solution for cross-platform, cross-cloud and cross-region data sharing in the areas of data, analytics and AI. Delta Sharing enables seamless sharing of live datasets, models, dashboards and notebooks, promoting collaboration without vendor lock-in. Integration with Databricks Unity Catalog provides robust security and governance. Testimonials highlight Delta Sharing’s role in simplifying data discovery, improving analytics and facilitating secure collaboration at scale.
Data Sharing and Collaboration With Delta Sharing
The eBook provides practical insights into developing a data sharing and collaboration strategy and emphasizes its importance in driving business value. It introduces Delta Sharing, an open source method that enables the secure sharing of data across platforms and geographies. The preview looks at data sharing models, industry use cases, challenges and solutions, giving readers a comprehensive understanding of the benefits and implementation of open data sharing. It highlights Delta Sharing as a breakthrough solution that enables seamless collaboration while ensuring strong security and governance measures.
Unlock the true value of your data
Databricks offers an open and secure solution for data sharing and collaboration. Discover common use cases and key benefits of modern data sharing. Learn about Delta Sharing, an open standard for secure sharing of live data and AI assets. Explore Databricks Marketplace, an open marketplace for data and AI solutions. Understand Databricks Clean Rooms, enabling secure collaboration with customers and partners. Get started and accelerate time to value by consuming shared data directly from your preferred
Data Governance
Data governance is critical to effectively managing data assets and ensuring quality, security and compliance throughout their lifecycle. By establishing standards and procedures, data integrity is improved and confidence in decision making is enhanced. Strong data governance practices enable reliable data pipelines and analytics platforms that are aligned with business objectives.
Unity Catalog
Databricks Unity Catalog optimizes governance for data and AI, enabling seamless management of structured and unstructured data, ML models, notebooks and more across clouds. The solution enables teams to securely access and collaborate on trusted assets, accelerate initiatives and simplify compliance. Testimonials highlight the solution’s role in simplifying access control, improving governance and centralizing data management. With AI-powered monitoring and open data sharing, Unity Catalog provides a unified solution for efficient data exploration, access management and operational intelligence.
Data, Analytics and AI Governance
The eBook addresses the challenges of data, analytics and AI governance in modern organizations and highlights the complexity created by disparate systems and data silos. It argues for a unified governance approach to effectively manage diverse data assets across multiple clouds. Key topics include the need for modern governance strategies from BI to ML, essential components of a successful governance framework, best practices for data lakehouse governance, and leveraging the Databricks Data Intelligence Platform for unified governance solutions.
How to simplify data and AI governance
The webinar highlights challenges in data and AI governance faced by organizations and introduces Databricks Unity Catalog as a solution. It offers a unified governance approach for all data and AI assets across various architectures and clouds, promoting consistency and efficiency. Key topics covered include the product roadmap, setup guidelines for Unity Catalog, management of user identities, access controls, audit logs, automated lineage, secure data sharing with Delta Sharing, and a real-world case study showcasing Milliman’s success with Unity Catalog.
Data Modeling
Data modeling is crucial for effectively organizing data, ensuring accuracy and supporting analysis. It creates a framework for defining data entities, attributes and relationships and facilitates efficient design. Clear data structures help maintain integrity, optimize queries and promote collaboration. By providing a common language for understanding data, organizations can gain insights and make informed decisions based on reliable data.
Data Modeling Best Practices & Implementation on a Modern Lakehouse
This comprehensive blog post looks at the intricacies of data modeling in Databricks Lakehouse. It describes the importance of data modeling in the context of building a data warehouse, highlighting the evolution from the semantic business information model to the logical data model and finally to the physical data model. The guide emphasizes the importance of proper system analysis and design to capture key business entities and attributes. Practical examples illustrate the implementation of dimensional modeling, including the creation of fact and dimension tables. In addition, essential techniques such as primary and foreign key definitions, identity columns for surrogate keys, and column constraints for data quality are covered so that data engineers have a solid foundation for effective data modeling practices in Databricks.
Data Warehousing Modeling Techniques and Their Implementation on the Databricks Lakehouse Platform
This blog post looks at data modeling within the Lakehouse paradigm, which combines data lakes and warehouses for versatile enterprise-level data management. It outlines the bronze/silver/gold principles of data organization and focuses on data vault and dimensional modeling techniques within each layer. Data Vaults, which include Hubs, Links and Satellites, provide for flexible data warehousing and fit well within the Silver tier. Dimensional modeling, exemplified by star schemas, optimizes data for analytics and is typically used in the gold layer. The resource focuses on aligning data organization with use cases, fostering collaboration and ensuring governance within the lakehouse architecture.
Data modeling in Databricks: maximum efficiency in data analysis
The blog post discusses the importance of data modeling in the context of Rittal’s transition to a smart factory and the challenges posed by the large amount of data generated on a daily basis. He introduces Databricks as a cloud-based platform and emphasizes its role in data modeling and analysis. Data modeling is presented as crucial for structuring and organizing data, defining integrity rules, facilitating analysis and fostering collaboration. Databricks offers various data modeling approaches such as databases, delta lake, schema development and performance optimization to improve the efficiency of data analysis. Ultimately, effective data modeling with Databricks enables organizations to gain valuable insights and successfully drive data-driven initiatives.
Visual data modeling using erwin Data Modeler by Quest on the Databricks Lakehouse Platform
The blog post describes the integration of erwin Data Modeler with Databrick’s Lakehouse Platform and highlights its importance for modernizing data assets and migrating to a scalable architecture that supports ETL, BI and AI. It highlights key benefits such as improved data understanding, accuracy, collaboration, performance and maintenance. Three scenarios demonstrate the benefits of erwin: the creation of new logical and physical models, the reverse engineering of existing models from Databricks and the migration of models from legacy systems. The article highlights the role of data modeling in improving governance, cost efficiency, collaboration and migration to cloud platforms such as Databricks. It concludes by inviting readers to explore erwin’s capabilities via Databricks Partner Connect.
Using Models and Tests with dbt and Databricks: Ensuring Data Quality and Accuracy!
The article describes how to use dbt’s modeling capabilities and testing framework to ensure data quality and accuracy in data transformation workflows within the Databricks integration. It guides data engineers through the definition of models by creating models and building on existing models, facilitating complex transformations. It also explains how to write tests to verify data integrity and accuracy, following a similar approach to defining tests for models. By performing tests, data engineers ensure the quality of data transformations. Ultimately, building interconnected models promotes modular and reusable structures that improve quality assurance in data transformation workflows.
Semantic Layer
An Intermediary Layer: The semantic layer is increasingly recognized as a vital component of modern data management. It functions as an intermediary that translates complex data structures into familiar business terms, providing a unified view of data across the organization. This layer simplifies data access by mapping disparate data sources into a consistent format, which addresses key challenges such as limited data availability, inconsistent reporting, and an overburdened IT department. By offering a single source of truth, the semantic layer ensures that data is accessible and understandable to all users, regardless of their technical expertise.
Mastering Semantic Layers: An understanding of the semantic level is becoming more and more important for data engineers. Knowledge of semantic layers makes it possible to design and implement solutions that improve data management, facilitate AI integration and streamline organizational processes. By integrating semantic layers into their skill set, data engineers can better support business needs, foster effective collaboration between technical and business teams, and contribute to more informed decisions and operational efficiency.
Using a Knowledge Graph: Implementing a semantic layer with knowledge graphs can improve data organization and accessibility by mapping complex relationships within data sets. This approach, used in integrations such as Databricks and Stardog, improves data management and insight generation by transforming data into actionable information. Knowledge graphs facilitate efficient data retrieval, support advanced analytics and improve decision making through meaningful, context-rich connections.
Implementing a Semantic Layer: Understanding how semantic layers are implemented enables data engineers to design systems that transform complex data structures into intuitive, business-friendly views and optimize data access for all users.
Data engineers should know how to integrate semantic layers into existing data platforms to optimize performance and scalability. This includes creating effective data models, managing workflows and enforcing data management policies. Mastering these aspects can significantly reduce IT effort, improve data quality and support advanced analytics and AI applications.
Enhancing AI Accuracy: The use of semantic layers in generative AI offers significant benefits for improving data accuracy and reliability. Semantic layers help mitigate the problem of hallucinations in AI outputs by providing a contextual framework that reduces errors and improves the accuracy of generated data. This is achieved through proper data cleansing, curation and modelling that contextualizes and harmonizes the data for more accurate AI responses.
Semantic layers increase the trustworthiness of AI outputs by providing a structured, business-oriented view of the data. By clearly defining metrics and metadata, semantic layers support large language models (LLMs) in generating correct queries and outputs, improving overall performance and reducing errors.
Future of Semantic Layers: The future of semantic layers will focus on improving data management through standardized definitions to increase reusability and composability for both human users and genAI models. Integration with AI and machine learning will drive predictive analytics and automate data tasks, while enhanced governance capabilities will improve data security and privacy. Semantic layers are expected to be used across industries and offer significant benefits by simplifying data analysis and accessibility.
Regulatory Compliance
Data engineers must be familiar with the legal requirements to ensure compliance with legal standards when processing data. This knowledge mitigates risks such as data breaches and legal consequences and promotes the transparency and integrity of data management.
Security and compliance guide
This guide describes the security features in the Databricks environment that enable enterprise data teams to improve their security posture according to their risk profile and governance policy. It covers authentication, access control, networks, data security, encryption, secrets management, auditing, privacy and compliance. It also introduces the Security Analysis Tool for evaluating and improving security configurations. It also provides resources for building a comprehensive security solution tailored to the organization’s needs.
Announcing new security controls and compliance certifications for Azure Databricks and AWS…
Databricks introduces an enhanced security and compliance add-on that provides PCI-DSS and HIPAA compliance for Azure Databricks and AWS Databricks SQL Serverless, with AWS also PCI and FedRAMP Moderate certified. The add-on includes Enhanced Security Monitoring for threat protection and Compliance Security Profile for compliance governance, simplifying regulatory compliance. It is available immediately and improves security controls for processing sensitive data.
Implementing the GDPR ‘Right to be Forgotten’ in Delta Lake
Mastering compliance with data regulations like GDPR and CCPA, specifically regarding the “right to be forgotten,” is essential for data professionals. This includes proficiency in techniques such as data anonymization and normalization within Delta tables. These skills ensure adherence to legal requirements while maintaining data integrity and usability. Additionally, understanding the implications of regulatory fines and penalties for non-compliance, as well as the importance of implementing retention policies and utilizing tools like Vacuum in Delta Lake, are critical aspects of data governance. Overall, this knowledge empowers professionals to effectively manage data in accordance with legal standards, mitigate risks associated with regulatory violations, and uphold privacy rights for individuals in the digital era.
Deepen Your Practice
Deepening their practice is critical for data engineers to improve their expertise and effectively tackle complex challenges. By deepening their area of expertise, data engineers can gain practical insights, optimize workflows and keep up to date with industry trends. This continuous learning process enables them to develop innovative solutions, improve data management practices and make a meaningful contribution to business success.
Databricks Solution Accelerators
Databricks Solution Accelerators are essential for data engineers, providing curated materials such as notebooks, code examples and best practices. They accelerate learning and improve implementation by providing structured frameworks and real-world examples that enable engineers to gain insights, overcome challenges and increase workflow efficiency.
Creating a Community of Industry Practitioners With Databricks Solutions Accelerators
Organizations need to efficiently transform raw data into valuable insights to be successful. Databricks’ solution accelerators facilitate this transformation, offering over 50 industry-specific tools and lakehouses. These accelerators, available on GitHub, enable users to tackle industry-specific challenges, foster collaboration and drive innovation. By leveraging these resources, organizations can improve decision making, develop new products, and accelerate growth in today’s data-driven landscape. Join the community to utilize these tools and shape the future of your industry together.
Databricks Engineering Blog — Solution Accelerators
The Engineering Blog discusses various topics related to data-driven approaches and modernization across different industries. Articles cover themes such as customer lifetime value estimation, risk management, quality of service analytics for streaming video services, safety stock analysis optimization, clinical health data lake construction, automation of digital pathology image analysis, and predictive maintenance implementation for oil and gas assets. Each post provides insights, solutions, and practical approaches to leveraging data analytics and machine learning techniques to address specific challenges and improve business outcomes in their respective sectors.
Databricks Solution Accelerators
Databricks Solution Accelerators streamline discovery, design, development and testing, saving hours of work. These purpose-built guides, which include fully functional notebooks and best practices, accelerate results for common and high-impact use cases. Users can go from idea to proof of concept (PoC) in as little as two weeks. Solution Accelerators are available to all Databricks customers at no additional cost and can be implemented via a free trial or an existing account. Their goal is to accelerate data and AI initiatives by providing customized resources and proven patterns.
Databricks Brickbuilder Program
Databricks Brickbuilder solutions, developed by leading consulting firms, address multiple industries and provide expertise in demand planning, risk management and more.
Brickbuilder Solutions: Partner-developed industry solutions for the lakehouse
Databricks introduces Brickbuilder solutions customized by leading consulting firms and built on the Databricks Lakehouse platform. These solutions support different stages of the customer journey and provide value acceleration, technical validation and global access in industries such as retail, finance, healthcare and media. Brickbuilder solutions provide expertise in areas such as demand planning, video quality analysis, risk management and healthcare data interoperability, enabling organizations to effectively meet specific business needs. Through collaboration with consulting partners, Databricks continues to expand its industry initiatives and platform migration solutions, ensuring comprehensive support for various use cases and industries.
Databricks Expands Brickbuilder Program to Include Lakehouse Accelerators
Databricks introduces Brickbuilder Accelerators, an extension of its program developed in collaboration with system integrators and consulting partners. These Accelerators leverage established frameworks and pre-built code to address critical analytical challenges to reduce costs and increase productivity. The initial focus is on Lakehouse Accelerators, which help organizations rapidly implement the Databricks Lakehouse platform. Compared to standard solutions, Accelerators offer focused engagements that are often completed within 30 days to quickly achieve data, analytics and AI goals.
Databricks adds new migration Brickbuilder Solutions to help customers succeed with AI
Databricks offers 60 partner solutions, spanning legacy system migrations, demand forecasting, customer 360, and risk management. The latest focus is on Brickbuilder’s migration solutions, ensuring a smooth transition to Lakehouse architecture. These solutions promise reduced risk, faster value realization, and higher ROI. Explore these migration solutions to optimize agility, adaptability, and performance in cloud environments.
Databricks Admin Essentials
Databricks Administration Knowledge is essential for the efficient management of Databricks environments. It enables administrators to set up teams, configure permissions, implement access controls, monitor usage and improve security.
Understanding the administration enables regular data engineers to optimize resource usage, monitor performance and collaborate effectively within the Databricks environment. More importantly, data engineers can implement cost-effective strategies, such as utilizing optimal cloud instance types, leveraging serverless compute and monitoring usage patterns, ultimately resulting in significant cost savings for their organizations.
Get started with Databricks administration
This tutorial provides instructions for new account and workspace administrators on how to use Databricks management and security features. It is recommended to set up teams, configure permissions, implement access control, monitor usage and improve security. Access control features require a Premium plan or higher. Additional security measures can be customized depending on the company’s needs. Support options include email help and access to Databricks Academy for self-paced learning or live training.
Functional Workspace Organization on Databricks
A key feature of the Databricks Lakehouse platform is the ability to create multiple workspaces from a single account to facilitate collaboration. However, this raised questions about workspace management. Key considerations include determining when to create a new workspace, effectively organizing workspaces, and implementing best practices. Balancing the benefits of isolation with the complexity of management is critical. Best practices include defining standardized processes, using least-privilege access models, leveraging sandbox environments, ensuring data isolation and implementing disaster recovery strategies. A well-planned workspace strategy is critical to maximizing the benefits of the Databricks Lakehouse platform.
Databricks Workspace Administration — Best Practices for Account, Workspace and Metastore Admins
This blog post discusses best practices for workspace management, including disaster recovery strategies with Terraform. It highlights the importance of effective workspace organization, which includes policy setup, resource regulation, data access and compute optimization. A distinction is made between account, workspace and metastore administrators and their respective responsibilities are described. Recommendations include cluster policies, consumption tracking and the use of Databricks features such as Delta Live Tables and Unity Catalog for efficient data management. Collaboration between administrators, project managers and users is emphasized as critical to success.
Best Practices for Cost Management on Databricks
Databricks Lakehouse platform provides users with flexible access to scalable computing resources that enable instant provisioning and horizontal scaling. However, this convenience can lead to uncontrolled cloud costs. Administrators try to balance cost control and user productivity. They achieve this through cluster policies that govern the creation and configuration of clusters. In addition, they manage costs by optimizing cloud instance types, using serverless compute and monitoring usage. These strategies ensure efficient resource utilization while controlling costs in all Databricks workspaces.
Serving Up a Primer for Unity Catalog Onboarding
Centralized governance enables flexible workspace management and reduces complexity. A simple customer journey illustrates onboarding to Unity Catalog (UC) and Identity Federation for centralized user and permission management, highlighting the roles of Cloud Admin, Identity Admin and various Databricks personas. Custom groups are recommended for scalable authorization management. The recipe includes collaboration, checklist, work sharing, cook steps and scenario examples, highlighting secure data access across workspaces and regions. Unity Catalog centralizes data management and promotes secure sharing and traceability.
Databricks Platform Administrator Accreditation
Candidates must understand administering users, managing compute resources, and governing and securing the Databricks Lakehouse Platform. This includes tasks like importing from identity providers, managing clusters and warehouses, configuring permissions, and implementing data governance. While not explicitly tested, candidates need a working knowledge of Python or Scala for the exam, available in both languages.
Conclusion
In today’s data-centric environment, the central role of data engineers in business success cannot be overstated. These professionals form the backbone of organizations by transforming raw data into valuable insights that drive strategic decision making and operational efficiency. Through their expertise in data management and analysis, data engineers bridge the gap between disparate data sets and strategic goals, foster cross-departmental collaboration and ensure alignment with overall business objectives.
As the complexity of data ecosystems continues to evolve, data engineers must adapt and expand their skills to stay at the forefront of innovation. Mastering new technologies such as stream processing and reverse ETL, combined with a deep understanding of data governance and regulatory compliance, enables them to navigate the intricacies of the modern data landscape with confidence and precision. Investing in continuous learning empowers data engineers to optimize processes, increase productivity and drive sustainable growth in an ever-changing digital landscape.
Data engineers are indispensable drivers of business success.
Data Engineers are indispensable catalysts for business success, enabling organizations to leverage the full potential of their data assets and thrive in today’s competitive marketplace. As champions of data-driven decision making, they use their expertise to help shape the future development of organizations across all industries.
In part three, discover strategies to stay updated on the latest trends and advancements in data engineering and related fields.
