Selected Data Engineering Posts . . . March 2024

A selected collection of my posts on data technology that have generated the most interest

Welcome to the second issue of our newsletter! We look forward to exploring the dynamic world of data technology and management with you.

The March 2024 edition of "Selected Data Engineering Post" delves into the dynamic field of data management and engineering. It highlights critical challenges and innovative solutions, exploring topics such as the influence of Generative AI on data management, the importance of data modeling, and the evolving role of Data Engineers in contemporary enterprises. Readers will gain valuable insights into how organizations tackle AI data management challenges, utilize brainwriting for innovation, and optimize marketing strategies using data insights. Join us as we delve into these transformative topics shaping the future of data engineering.

Highlights of this issue:

• Data Management Challenges in the Era of Generative AI: Explore the challenges and opportunities presented by Generative AI, highlighting the critical importance of data quality and integration.
• Brainwriting for Data Innovation: Address challenges in group dynamics by employing brainwriting techniques to accelerate idea generation.
• Monetary Value of Data Modeling: Enhance decision-making processes and communication efficiency while minimizing maintenance costs through structured data modeling.
• The Pivotal Role of Data Engineers: Discuss the importance of collaboration and advancements in data processing techniques to achieve operational efficiency.
• Data Modeling and Streaming Architectures: Harness the potential of data for business success in today's dynamic landscape through effective modeling and streaming architectures.
• Delta Lake vs. Data Lake – A Comparison: Analyze the advantages of Delta Lake over traditional Data Lakes to enhance data management practices.
• The Vital Partnership of Data Science and Data Engineering: Emphasize the significance of collaboration between data science and data engineering to ensure effective data utilization and decision-making.
• How to Become a Data Engineer: Explore the efficiency and flexibility of Databricks for managing complex data environments, offering insights into becoming a proficient data engineer.
• Enriching Marketing Strategies with Data Insights: Implement data-driven strategies to improve customer experiences while addressing concerns related to data quality and privacy.

Whether you're a data engineer, a data analyst, an AI expert or just someone interested in the latest tech trends, this newsletter issue is for you! Enjoy reading!

???????? ???????????????????? ???????????????????? ???? ?????? ?????? ???? ???????????????????? ????

As organizations transition into the era of Gen AI, the relationship between data management and AI becomes paramount. This article explores the challenges and opportunities presented by Gen AI, shedding light on the indispensable role of robust data management practices.

?????? ???????????????????? ?????? ?????? ???? ???????? ????????????????????

Managing ???????? ?????????????? emerges as a primary challenge. The reliability of AI outputs hinges on the quality of input data, making data validation crucial. Additionally, handling the vast volumes of data required for custom Gen AI models poses infrastructure and energy consumption challenges.

?????????????? ???????????????? loom large, especially with the reliance on sensitive data for personalized AI applications. Ensuring data privacy necessitates transparent policies, reliance on third-party data sources, and innovative solutions like synthetic data generation.

???????? ?????????????????????? emerges as another hurdle, with Gen AI applications requiring synthesis of diverse data sources. Seamless integration demands technological solutions and poses compatibility and processing efficiency challenges.

?????????????????? ???????? ?????????????? ???? ?????? ????: Establishing clear data quality standards and implementing controls at data capture points are imperative. Addressing data quality issues upstream at the source is essential. Moreover, monitoring and evaluating Gen AI outputs are crucial to counter phenomena like "hallucination."

???????????????????? ???????? ?????????????????????? ????????????????????: Proactively addressing privacy concerns through transparent policies and leveraging third-party or synthetic data sources can alleviate data acquisition challenges.

???????????????? ???????? ???????????????????? ??????????????????????: Foundational data management capabilities form the bedrock for successful Gen AI deployment. Enterprise-wide strategies, defined roles, and use-case-specific capabilities are essential for effective activation.

???????????????????? ?????? ???? ???? ???????? ????????????????????: AI-powered integration tools streamline data processing and analysis, while Gen AI itself aids in interpreting unstructured information and generating consistent data definitions.

????????????????????: As organizations navigate the Gen AI landscape, strategic data management practices are pivotal. Prioritizing privacy, efficiency, and innovation ensures successful Gen AI deployment and maximizes its transformative potential.

Go to Article

Additional References

Data Governance in the Era of Generative AI

How data governance must evolve to meet the generative AI challenge

Preparing your data strategy for the Generative AI era

?????????????? ???? ????????: ?? ???????? ???? ????????????

Apache Spark 3.4 and 3.5: Enhancing PySpark Performance and Flexibility

Apache Spark releases 3.4 and 3.5 in 2023 brought significant improvements to PySpark, focusing on performance, flexibility, and ease of use. Here's a breakdown of key features:

?????????? ??????????????: Introduces remote connectivity for Spark clusters, enhancing stability and observability.

??????????-?????????????????? ???????????? ????????: Doubled performance of Python UDFs by leveraging the Arrow columnar format.

???????????? ??????????: Introduced user-defined table functions for table-based transformations natively in PySpark.

?????? ?????????? ?????? ????????????????: Included GROUP BY ALL and ORDER BY ALL for seamless integration with PySpark.

???????????? ?????????????????? ???????????????? ????????????????????: Unlocked real-time analytics with state processing in Structured Streaming.

????????????????????????????????: Enabled distributed PyTorch training on Spark clusters for deep learning models.

?????????????? ??????: Facilitated easier testing for PySpark applications with detailed error messages.

?????????????? ??????: Simplified PySpark programming by allowing commands in plain English.

These improvements empower data professionals to efficiently leverage PySpark for diverse data processing tasks. With Apache Spark 4.0 on the horizon, further advancements are anticipated to revolutionize data analytics workflows.

Go to Article

???????????? ???? ?????????????? ?????????? ????????

?????????????????? ???? ???????????????????? ???????? ????????????????: ?? ?????????????????? ?????? ???????? ?????????????????? ?????? ??????????????????

??????????, a key element in data-driven applications, is characterized by its ability to process large volumes of data with real-time capabilities. However, Kafka streaming also presents challenges, especially when practical aspects are ignored. ?????? ???????????????? ???????????? ???? ?????????????? ???????? ?????? ???????????? ?????????? ???? ?????????? ????????????, which risks data loss for analytical tasks that depend on historical data.

?????????? ?????? ????????????????????: Log compaction selectively removes records while preserving the last update for each primary key. Ensures preservation of the last known value for each message key within the log for a single topic partition.

???????? ???? ???????? ??????????????: Log compaction is suitable for applications that focus on current state and is a challenge for tasks that require historical data, such as user behavior analysis.

???????? ?????? ?????????????????? ???????? ??????????????????:

• Use scalable storage solutions from AWS, Azure or GCP to instantly store the data without additional processing.
• Choose a streaming-friendly data format like Delta with features like Time Travel and Schema Evolution.
• Partitioning data by date and topic is essential for better accessibility.
• End-to-End Responsibility: Assign data archiving to the same team responsible for Kafka data production to ensure a cohesive approach.

Archiving Kafka data is not only a best practice, but essential for consistent insights and to ensure the long-term reliability and efficiency of data-driven applications.

Go to Article

???????????? ???????? ????????????????????

????????????????, ????????????????, ?????? ?????????????????? ????????????????????

Modern data warehouses offer solutions to ???????????? ?????? ?????????????? ???????????????????? ???? ???????? ???? ??????????'?? ???????????????? ??????????????????????.

??????????-?????????? ?????????????????? provide scalability, flexibility, and advanced analytics, empowering organizations with deeper insights and real-time data analysis. Features such as ELT, diverse data integration, and scalability enhance efficiency, while data virtualization and democratization foster seamless access and inclusivity.

?????????????????? ???? ???????????? ???????????????????? requires alignment with stakeholders, robust metadata management, and adaptation to evolving business needs. Companies are transitioning from traditional systems to cloud-based solutions like Databricks and Snowflake for improved data management.

Go to Article

Additional References

Elements of a Modern Data Warehouse

The modern data warehouse: Benefits and migration strategies

Modern Data Warehouse: All You Need to Know

Coursera - Learn Data Warehouse Online

???????????????????? ????????????????????

???????????????????????? ?????? ???????? ?????????????????? — ?????? ?????? ???????? ?????? ????????

In data engineering, staying innovative is crucial. Brainwriting, a collaborative ideation method, is gaining traction as it fosters creativity and diverse perspectives necessary for solving complex problems.

?????????????????????? ?????????????????????????? ???????????????????? ?????????????? ???????????????????? in group dynamics and behavior, such as:

• Difficulty in maintaining focus and attention.
• Dominance of strong personalities, hindering idea sharing.
• Tendency to stick with initial ideas, limiting innovation.
• Self-censorship based on others' reactions.
• Lengthy process due to large group participation.

???? ????????????????, ???????????????????????? ???????????? ?????????????????????? ????????????????:

• Inclusivity: Encourages participation from all team members, including introverts.
• Diverse Perspectives: Facilitates idea generation from varied backgrounds.
• Reduced Groupthink: Allows independent expression, fostering creativity.
• Enhanced Focus: Eliminates verbal distractions, promoting concise idea formulation.
• Efficient Idea Generation: Accelerates the brainstorming process, leading to a wealth of innovative solutions.

???? ?????????????????????? ?????? ???????????????????????? ???? ?????????????????????????? ?????? ?????????????????? ?????? ???????????????? ???? ????????????????????????, ?????????? ?????? ?????????????? ?????????? ???????? ???????????????? ??????????????????, ?????????? ???????????????????? ???????????????????? ?????? ???????? ?????????? ?????????????????? ???????? ??????????????????????.

Go to Article

Additional References

How to Use Brainwriting to Generate Ideas

What is brainwriting?

Brainstorming vs. Brainwriting: Unleashing Ideas in Tech Teams

?????? ???????????????? ?????????? ???? ???????? ????????????????

... to optimize their system maintenance costs and increase operational efficiency

In the fast-paced world of information technology, ???????? ???? ???? ?????????????????? ???????????????????? ???? ???? ???????????? ???????????????? ???????????????????? ?????? ???????????? ????????????????-???????????? ??????????????????.

Amidst this dynamic landscape, data modeling proves to be a crucial technique that provides a structured approach to understanding and organizing data elements. This article looks at the ?????????????????????????? ???????????????????? ???? ???????? ???????????????? and highlights its central role in efficient database design and system maintenance.

?????? ???????????????????? ???? ???????? ????????????????: Data modeling provides the foundation for effective database design and ensures data accuracy, consistency and accessibility. By defining business concepts and organizing data elements, it provides a blueprint for data organization and usage.

???????????????? ?????? ????????????????????????: A well-designed data model improves decision-making, enhances communication and facilitates reusability across projects. Conversely, neglecting data modeling can lead to data inconsistencies, poor system performance and increased maintenance costs.

???????????????? ??????????: Even if the monetary value of data modeling is underestimated, its impact on reducing system maintenance costs is significant. By accurately capturing data requirements and minimizing errors during the development phase, companies can achieve significant savings in maintenance costs over the lifecycle of the system.

???? ??????????????, understanding the true value of data modeling is critical for companies looking to optimize their system maintenance costs and increase operational efficiency. By effectively incorporating data modeling techniques, ?????????????????????????? ?????? ?????? ???????? ???????????????????? ?????????? ???????? ???????????????????? ??????????????????, ?????? ???????? ?????????????? ?????????????????????? ???????? ??????????????, ???????????????????? ?????????????? ???? ????????-???????? ?????????????????? ????????????????.

Go to Article

Additional References

THE ROI OF DATA MODELING

What is Data Modeling (And Why Is It important)?

Data Marketplace - From Data Mesh's Missing Component to Real-Time Data Auctions

?????? ???? ???????????? ?? ???????? ????????????????

???????? ???? — ???????????? ???????? ????????????

In today's data-driven world, ???????? ?????????????????? ?????? ?????????????? ???? ???????????????? ??????????????. They use their expertise to organize and analyze data and turn it into actionable insights. Collaboration skills are essential to align data work with strategic goals.

?????????????? ?????? ???????????????? ??????????????: Data Engineers play a critical role in driving business success by organizing and analyzing data to generate actionable insights, complement data science efforts and support strategic objectives.

???????????? ??????????????????: Continuously learn and master areas such as stream processing, data warehousing, reverse ETL, data modeling and data governance.

???????????? ????????????????: Apply new skills using resources such as Databricks Solution Accelerators and Brickbuilder Solutions to optimize implementation and meet industry needs.

???????????????????????????? ????????????????????????: Develop Databricks administration skills to optimize operations and ensure efficient resource utilization, secure data access and efficient cost management.

Go to Article

Additional References

What Skills Does a Data Engineer Need?

Essential Data Engineering Skills for : 15+ Must-Have Abilities

Data Engineer Skills 101: Everything You Need to Know For a Career in Data Engineering

Unleashing Innovation: Brainwriting for Data Engineers — but not just for them

?????????????????? ?????????????? ?????????????? ???????????????????? ???????? ?????????????????? ???????????????? ??????????????????

A recent ???????????? ???? ???????????? ??????????? ??.?? ?????? ???????????????????? ?????????????? ????.?? introduces PySpark DataFrame equality test functions, streamlining unit testing processes. The ?????????????????????? ???????????? ?????????? ??.?? ?????? ???????????????????? ?????????????? ????.?? ???????? ???????????? ?????????? ????????????????, enhancing PySpark code reliability.

?????????????????? ???????????????? ???????? ?????????????????? ?????? ?????????????????? ??????????????????????????????: PySpark data manipulation involves complex transformations, raising concerns about code accuracy. The new equality test utility functions validate data against expected outcomes, offering concise insights into discrepancies and expediting error detection during analysis.

?????????????????? ?????????????????? ???????????????? ???????? ??????????????????: Introduced in Apache Spark 3.5, assertDataFrameEqual and assertSchemaEqual enable seamless DataFrame comparison, simplifying debugging. With assertDataFrameEqual, a single line of code assesses DataFrame equality, while assertSchemaEqual validates schema coherence across DataFrames.

???????????????????? ???????????? ?????? ??????????????????: These functions provide detailed output, aiding debugging efforts, and facilitate debugging of DataFrames beyond unit testing scenarios.

???????????? ?????? ???? ?????????? ???????????????? ???????? ??????????????????: Additional equality test functions for Pandas API on Spark ensure compatibility checks between different DataFrame libraries, fostering seamless data analysis across platforms.

These enhancements optimize PySpark testing efficiency, ensuring code robustness for data manipulation tasks.

Go to Article

?????????????????? ????????????????????!

?????????????????? ?????? ?????????? ???? ?????????????????? ?????????????????????????? ???????? ????????????????????

In today's data-driven landscape, the need for efficient data processing is paramount. ?????????????????????? ?????????? ???????????????????? ?????????????? ???????????? ???????? ???????? ???????? ?????? ?????????????????????? ???????????? ???? ???????? ????????????, ???????????????? ?????? ??????????????. To meet this challenge, companies are increasingly turning to streaming architectures.

?????????????????? ?????????????????????????? ?????????? ?????? ?????????????????????? ???????? ???????????????????? ???? ???????? ??????????????, eliminating the need to wait for large batches of data to accumulate. By leveraging Spark Structured Streaming on the Databricks Data Intelligence Platform, teams can ???????????????? ????????????????????, ?????????????? ?????? ???????? ???????????????????? ?????????? ???????????????????????? ???????? ????????????????????, ?????????????????????? ?????? ???? ????????????????????????.

?????? ???????????????? ???? ?????????? ???????????????????? ?????????????????? include scalability, simplicity, data freshness and cost efficiency. With Databricks, users can seamlessly transition from batch to streaming processing, ensuring real-time insights and improved operational efficiency.

To ?????????????? ?????????????????? ?????????????????????????? ???? ????????????????????, users can visit the product pages for streaming and Delta Live Tables, read customer success stories and access technical documentation to get started.

Streaming architectures on Databricks provide a future-proof solution for big data processing that offers unparalleled performance and significant cost savings.

Go to Article

Additional References

What’s New in Data Engineering and Streaming - January 2024

Streaming on Databricks

How We Performed ETL on One Billion Records For Under $1 With Delta Live Tables

???????? ???????? ????. ?????????? ???????? – ?? ???????????????? ????????????????????

?????? ???? ?? ?????????? ???????? ???? ???????????????????? ?? ???????????? ?????????????

?????????? ????????, the successor to Data Lake, revolutionizes data management, offering superior features and benefits.

When compared to traditional Data Lakes, ?????????? ???????? ???? ???????????????????? ?????????????? ???? ?? ?????????? ????????????, providing enhanced performance, integration, governance, and scalability. Databricks' unified platform seamlessly integrates with Delta Lake, offering unparalleled advantages.

???????? ????????????????????, ?????????????????????????? ?????? ?????????? ???????? ??????????, ???????????????????? ??????????????????, ?????? ???????????????? ???????????????????????? ???????????? ???????? ?????????????????????? ?????? ???????? ?????????????? ??????????????.

The platform's scalability and flexibility ensure efficient storage and processing of vast datasets, while its managed solution ?????????????? ?????????????????????? ????????????, ???????????????? ???????? ?????????????????????????? ???? ?????????? ???? ???????? ??????????.

As enterprises strive for real-time analytics and agile decision-making, Delta Lake on Databricks becomes the go-to solution, shaping the future of data management.

Go to Article

Additional References

Delta UniForm: a universal format for lakehouse interoperability

What about Apache Hudi, Apache Iceberg, and Delta Lake?

What about Apache Hudi, Apache Iceberg, and Delta Lake? II: Universal Format and Liquid Clustering with Delta Lake

Apache XTable: Seamlessly interoperate cross-table between Apache Hudi, Delta Lake, and Apache Iceberg

?????? ?????????? ?????????????????????? ???? ???????? ?????????????? ?????? ???????? ??????????????????????

When it comes to data, understanding the difference between data science and data engineering is crucial to a company's success. ?????? ?????? ???????? ?????????????????? ?????? ?????????????? ???????????????????

???????????????????????????? ??????????: Data Science extracts insights from data and provides descriptive, predictive and prescriptive analytics. Data Engineering creates and manages the infrastructure to ensure data reliability, scalability and accessibility.

?????????????????????????? ??????????????: Data scientists rely on data engineers to provide clean, organized data for analysis. Without proper data engineering support, data science projects can fail due to unreliable data.

?????? ????????????????????????????????: Data scientists focus on analytics, machine learning and data visualization. Data engineers take care of data infrastructure, ETL processes and data modeling.

When companies recognize and ???????????? ?????????????????????????? ?????????????? ???????? ?????????????? ?????? ???????? ??????????????????????, they can effectively leverage data for strategic decision-making and long-term success.

Go to Article

Additional References

The Vital Roles of Data Science and Data Engineering

How Data Science and Data Engineering Work Together in Custom Software Projects

Data Science Needs Close Collaboration with Data Engineering to be Effective

?????? ???? ???????????? ?? ???????? ???????????????? … (??????????????)

???????? ?? — ?????? ????????????????????????

???????????????? ????????????????????’ ?????????????????????????? ???????????????? ?????????????????? to gain practical hands-on experience in data engineering and analytics.

?????? ????????????????????’ ?????????????????????????? ???????????????? to validate your skills and expertise as a data engineer and improve your credibility and marketability with potential employers.

Data engineers need expertise in platform management, ETL processes, data processing, pipelines and governance for robust solutions. Mastering these skills is critical to enterprise data initiatives and passing the ???????????????????? ?????????????????? ???????? ???????????????? ?????????????????? exam.

Professional Data Engineers are characterized by their skills in tooling, data processing, security, governance and testing. These skills are essential for stable solutions. Passing the ???????????????????? ?????????????????? ???????? ???????????????? ???????????????????????? exam requires mastery of advanced tasks with the appropriate tools.

?????????? ???? ????????????????

• Why Databricks?
• Data Engineer
• Data Lakehouse
• Data Engineering — The Basics
• Data Engineering — Advanced Techniques

· ?????????????????????????????? ?????? ????????????????

• Creating Your Resume
• Preparing for Interviews

Go to Article

?????????????????? ?????????????????? ???????? ???????? ????????????????

In today's digital age, successful marketing relies heavily on data-driven insights. ???????? ?????????????????????? ?????????? ?? ?????????????? ???????? ???? ???????????????????? ?????? ?????????? ???? ???????? ?????? ?????????????????? ????????????????????:

?????? ???????? ???? ???????? ?????????????????????? ???? ??????????????????:

• Customer Segmentation
• Personalized Recommendations
• Data Collection & Integration
• Marketing Automation
• Improved Customer Experience
• Competitive Analysis
• Social Media Engagement

????????????????????:

• Data Quality and Integration
• Data Privacy & Compliance
• Real-Time Data Analysis
• Organization Silos
• Talent & Skill Gap

?????????????????? ?????????????????? ???????????????? ?????????? ???? ????????:

• Set Clear Business Goals
• Gather & Analyse Data
• Identify Target Audience
• Competitor Analysis
• Choose the Right Channels
• Measure and Track KPIs

???????? ?????????????????????? ???? ???? ???????????????????? ?????? ?????????????????????? ???????? ???? ???????????? ??????????????????.

Go to Article

Additional References

How to Use Data Analytics to Improve Your Marketing Strategy

Using Data Driven Marketing to Improve Your Business

Coursera - Customer Data Analytics for Marketers

Coursera - Data Analytics Methods for Marketing

Takeaways

Transitioning into the era of Generative AI: Proactively implement robust data management strategies to address challenges like data quality, privacy, and integration, ensuring successful deployment of Generative AI.

Efficiently managing Kafka data archives: Employ strategic storage solutions and take end-to-end responsibility to balance real-time processing and historical data accessibility for data-driven applications.

Modern data warehouses: Utilize cloud-based platforms for scalable, flexible, and analytics-driven solutions, enhancing insights and efficiency in managing complex business data.

Brainwriting: Improve innovation outcomes by promoting inclusivity, diverse perspectives, and efficient idea generation through brainwriting in data engineering environments.

Understanding the importance of data modeling: Optimize system maintenance costs and increase operational efficiency by adopting structured data modeling approaches, improving decision-making, communication, and reusability.

Continuous skill development for Data Engineers: Enhance expertise in stream processing, data modeling, and tools like Databricks for efficient workflows and resource management.

Streaming everything: The transition to a "streaming everything" architectures, such as Spark Structured Streaming on Databricks, facilitates efficient data processing and provides real-time insights and improved operational efficiency in the data-driven landscape.

The Vital Partnership of Data Science and Data Engineering: Recognize the distinct roles of data science and engineering to ensure reliable, scalable data management infrastructure for strategic success.

Delta Lake: Upgrade from traditional Data Lakes to Delta Lake for improved performance, integration, governance, and scalability, seamlessly integrated with Databricks' unified platform for efficient storage, processing, and real-time analytics.

Enhancing Marketing Strategies with Data Insights: Leverage data engineering for customer segmentation, personalized recommendations, compliance, and real-time analysis, ensuring data quality and strategic decision-making in modern marketing practices.

Thank you for exploring this month's edition of "Selected Data Engineering Posts". Wishing you ongoing success and looking forward to reconnecting in our next issue.

Axel Schwanke

#DataMarketplaces #DataMesh #Lakehouse #Certification #BusinessAlignment #DigitalTransformation #OReilly #ReverseETL #LeadManagement #Marketing #MarketingInsights #DataDrivenMarketing #ACIDTransactions #SparkStructuredStreaming #DataFrameEquality #PandasAPI #TestingFrameworks #Learning #DataInsights #BrainStorming #Ideation #ModernDataAnalytics #BusinessIntelligence #CloudMigration #DataArchiving #Kafka #KafkaStreaming #TechInsights #LLM