Master the concepts of modern data architecture. Learn to design, evaluate, and choose the right patterns for any cloud
β±οΈ Length: 1.3 total hours
β 4.11/5 rating
π₯ 10,409 students
π July 2024 update
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- Course Overview
- Explore the historical evolution of data management from traditional On-Premise Relational Database Management Systems (RDBMS) to modern, highly scalable, and distributed cloud-based storage solutions that accommodate the 3Vs of Big Data: Volume, Velocity, and Variety.
- Understand the paradigm shift from ETL (Extract, Transform, Load) to ELT (Extract, Load, Transform) and how this transition allows organizations to store raw data in its native format before defining a specific schema, thus preserving maximum data fidelity for future use cases.
- Analyze the fundamental concept of “Compute and Storage Decoupling,” learning how this modern architectural principle enables independent scaling of resources, leading to significant cost savings and improved performance for enterprise-level data operations.
- Dive into the strategic importance of building a “Single Source of Truth” within an organization to break down departmental data silos and foster a data-driven culture that relies on a unified repository for all digital assets.
- Examine the role of the Data Lake in the broader Modern Data Stack, specifically how it serves as the foundational layer for downstream applications such as Advanced Analytics, Artificial Intelligence (AI), and Machine Learning (ML).
- Discuss the architectural trade-offs between various storage paradigms, including the trade-offs between performance, consistency, and availability in distributed systems (the CAP Theorem) as they apply to massive-scale data environments.
- Gain insights into the “Medallion Architecture” framework, exploring how data flows through refined stagesβfrom raw landing zones to cleaned, enriched, and finally aggregated business-ready datasets.
- Requirements / Prerequisites
- A foundational understanding of database management systems (DBMS) and basic familiarity with Structured Query Language (SQL) to understand how data is traditionally queried and organized.
- General knowledge of Cloud Computing principles, particularly an awareness of major service providers like Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
- Basic comprehension of the data lifecycle, including how data is generated by applications, moved through networks, and eventually consumed by business users or automated systems.
- An introductory understanding of file formats and data types, such as the difference between structured data (tables), semi-structured data (JSON, XML), and unstructured data (images, videos, logs).
- Prior exposure to fundamental IT concepts like server-client architecture, networking basics, and security protocols (such as IAM or encryption) will be beneficial but is not strictly mandatory.
- A mindset geared toward problem-solving and architectural thinking, as the course focuses on high-level design patterns rather than deep-dive programmatic coding.
- Skills Covered / Tools Used
- Cloud Storage Engines: Exploration of industry-standard objects stores such as Amazon S3, Azure Data Lake Storage (ADLS) Gen2, and Google Cloud Storage as the primary physical layers for data persistence.
- Open Table Formats: Introduction to modern storage layers like Apache Iceberg, Delta Lake, and Apache Hudi that bring ACID transactions and time-travel capabilities to standard data lakes.
- Big Data Processing Frameworks: Overview of the utility of Apache Spark, Hadoop MapReduce, and Presto/Trino for distributed processing and high-performance querying of petabyte-scale datasets.
- Data Cataloging: Use of tools like AWS Glue Data Catalog or Apache Hive Metastore to maintain a comprehensive inventory of metadata, making data discoverable and understandable for end-users.
- File Format Optimization: Comparison and selection criteria for columnar storage formats like Apache Parquet and Apache ORC versus row-based formats like Apache Avro to optimize for read/write performance.
- Partitioning and Indexing: Mastering logical data organization techniques to minimize data scanning during queries, thereby reducing latency and cloud consumption costs.
- Compute Engines: Familiarity with serverless query engines such as Amazon Athena or Azure Synapse Serverless for executing ad-hoc analysis without the need for managing underlying infrastructure.
- Benefits / Outcomes
- Develop the ability to design future-proof data infrastructures that can seamlessly scale from gigabytes to petabytes without requiring a complete re-architecture of the system.
- Achieve significant operational cost reductions by learning how to implement tiered storage strategies, moving infrequently accessed “cold” data to cheaper storage classes.
- Enhance organizational agility by enabling faster “time-to-insight,” allowing data scientists and analysts to access raw data immediately rather than waiting for lengthy ETL development cycles.
- Empower your organization to leverage advanced analytics and predictive modeling by providing a robust environment where ML models can be trained on massive, diverse datasets.
- Position yourself as a key asset in any technical team by mastering the vocabulary and design patterns required to lead digital transformation projects and cloud migration initiatives.
- Gain a competitive edge in the job market for roles such as Data Architect, Big Data Engineer, or Cloud Solutions Architect by bridging the gap between theoretical knowledge and practical design.
- Acquire the framework needed to evaluate third-party data tools and vendors objectively, ensuring that your organizationβs technology stack aligns with its long-term strategic goals.
- PROS
- Time-Efficient Learning: The course distills complex architectural concepts into a concise 1.3-hour format, making it ideal for busy professionals and decision-makers.
- Platform Agnostic: The principles taught are applicable across all major cloud providers, ensuring the skills are transferable regardless of the specific technology stack used by an employer.
- High-Level Strategic Focus: By concentrating on design and architecture rather than specific syntax, the course provides a “big picture” view that is often missing from purely technical tutorials.
- Up-to-Date Content: With the July 2024 update, the material reflects current industry trends, including the latest advancements in data lakehouse patterns.
- CONS
- Depth vs. Breadth: Due to the relatively short duration, students looking for intensive, hands-on coding labs or deep-dive configuration walkthroughs for specific tools may find the coverage to be more conceptual than technical.
Learning Tracks: English,Development,Database Design & Development
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