bundle-ultimate—sap-finance-and-sap-trm-fico—bpc—trm—s4hana-finance—s4hana-trm By Uplatz<\/a><\/h3>\nFurthermore, a comprehensive performance and economic analysis is conducted. The report examines quantitative benchmarks that position Delta Lake favorably against other open table formats like Apache Iceberg and Apache Hudi on S3. It outlines critical optimization strategies, including file compaction, Z-ordering in Delta Lake, and shared caching mechanisms in DVC, to maximize performance and minimize latency. The total cost of ownership is modeled, moving beyond simple storage pricing to include the often-overlooked costs of API requests, data transfer, and compute, providing a framework for effective cost management.<\/span><\/p>\nFinally, the report situates this stack within the broader technology ecosystem by providing a comparative analysis of alternatives for object storage, data versioning, and open table formats. This contextualization equips data architects with the necessary information to make informed decisions tailored to their specific use cases. The report concludes with a set of strategic recommendations for implementation, management, and governance, emphasizing that the successful deployment of this stack relies on understanding the complementary roles of each component and supporting the distinct workflows of data engineering and machine learning teams.<\/span><\/p>\n <\/p>\n
I. The Foundation: Scalable Object Storage with Amazon S3<\/b><\/h2>\n
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The modern data stack is built upon a foundation of scalable, durable, and cost-effective storage. Amazon Simple Storage Service (S3) has emerged as the industry’s de facto standard for this foundational layer, enabling the creation of vast data lakes that serve as the single source of truth for enterprises. Its design principles and feature set are not merely incidental but are the enabling technology for higher-level systems like Delta Lake and DVC. Understanding S3’s architecture is paramount to architecting any robust, large-scale data platform.<\/span><\/p>\n <\/p>\n
1.1 Architectural Underpinnings of a Global-Scale Storage System<\/b><\/h3>\n
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At its core, Amazon S3 is an object storage service, a fundamental distinction from traditional hierarchical file systems. This architectural choice is the key to its immense scalability.<\/span><\/p>\n <\/p>\n
Core Concepts<\/b><\/h4>\n
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The S3 data model is intentionally simple, comprising three main components <\/span>1<\/span>:<\/span><\/p>\n\n- Objects:<\/b> The fundamental entities stored in S3. An object is a file of any type and its associated metadata. Objects can range in size from a few bytes up to 5 TB.<\/span>1<\/span><\/li>\n
- Buckets:<\/b> Logical containers for objects. When creating a bucket, a globally unique name and an AWS Region must be specified. A bucket can store a virtually unlimited number of objects.<\/span>1<\/span><\/li>\n
- Keys:<\/b> The unique identifier for an object within a bucket. The key is analogous to a filename and provides the mechanism to retrieve a specific object.<\/span>1<\/span><\/li>\n<\/ul>\n
S3 operates with a flat namespace, meaning there are no native directories or folders in the way a traditional filesystem has. The folder-like structures seen in the AWS console or other tools are a user interface convenience. They are created by using a common prefix in the object keys (e.g., logs\/2024\/01\/event.json). While this simplifies the storage architecture, it has profound implications for operations like listing files, which require scanning all object keys with a given prefix rather than simply reading a directory’s metadata.<\/span>4<\/span> This very limitation is a primary driver for the development of metadata-aware table formats like Delta Lake, which are designed to overcome the performance bottlenecks of file listing on object stores.<\/span><\/p>\n <\/p>\n
Distributed by Design<\/b><\/h4>\n
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The remarkable durability and availability of S3 are direct results of its distributed architecture. When an object is uploaded to S3, it is not stored on a single disk or server. Instead, the service automatically creates and stores copies of the object across multiple, physically distinct Availability Zones (AZs) within the selected AWS Region.<\/span>1<\/span> An AZ is one or more discrete data centers with redundant power, networking, and connectivity. This inherent redundancy ensures that data remains accessible even if an entire data center fails. It is this design that allows S3 to reliably store hundreds of trillions of objects globally and forms the bedrock upon which the reliability of higher-level data systems is built.<\/span>1<\/span><\/p>\n <\/p>\n
1.2 Dissecting the Pillars of S3: Durability, Availability, Security, and Performance<\/b><\/h3>\n
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The value proposition of S3 rests on four key pillars that make it suitable for enterprise-grade, mission-critical data storage.<\/span><\/p>\n <\/p>\n
Durability and Availability<\/b><\/h4>\n
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S3 is designed for 99.999999999% (11 nines) of data durability.<\/span>1<\/span> This extraordinary figure means that if one were to store 10 million objects in S3, one could expect to lose a single object, on average, once every 10,000 years. This level of durability is achieved through the multi-AZ data replication described previously, combined with periodic, systemic data integrity checks. S3 uses control hash values to verify the integrity of data at rest and automatically repairs any corruption using its redundant copies.<\/span>3<\/span> Without this foundational guarantee that the underlying bits will not be lost, the integrity of systems built on top, such as Delta Lake’s transaction log, would be fundamentally compromised.<\/span><\/p>\nIn addition to durability, S3 offers high availability, backed by a Service Level Agreement (SLA) of 99.99% for the S3 Standard storage class.<\/span>3<\/span> For disaster recovery scenarios that require resilience against regional-level outages, S3 provides cross-region replication, which can automatically and asynchronously copy objects to a bucket in a different AWS Region.<\/span>3<\/span><\/p>\n <\/p>\n
Security and Compliance<\/b><\/h4>\n
<\/p>\n
Security is a paramount concern for data platforms. S3 provides a multi-layered security model to protect data from unauthorized access. By default, all new buckets and objects are private.<\/span>2<\/span> Key security features include:<\/span><\/p>\n\n- Encryption:<\/b> S3 automatically encrypts all new objects at rest by default. It also supports encryption of data in transit using SSL\/TLS.<\/span>3<\/span><\/li>\n
- Access Control:<\/b> Fine-grained access permissions can be configured using AWS Identity and Access Management (IAM) policies, bucket policies, and Access Control Lists (ACLs).<\/span>1<\/span><\/li>\n
- Public Access Prevention:<\/b> The S3 Block Public Access feature provides a centralized way to prevent public access to buckets and objects at the account or individual bucket level, acting as a critical safeguard against accidental data exposure.<\/span>1<\/span><\/li>\n
- Auditing and Monitoring:<\/b> AWS provides extensive auditing capabilities, allowing organizations to monitor access requests to their S3 resources, which is essential for security analysis and compliance.<\/span>1<\/span><\/li>\n<\/ul>\n
S3 also meets a wide range of compliance standards, including PCI-DSS, HIPAA\/HITECH, and FedRAMP, enabling its use in highly regulated industries.<\/span>1<\/span><\/p>\n <\/p>\n
Performance<\/b><\/h4>\n
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Designed for data-intensive applications, S3 offers high throughput and low latency access to data.<\/span>1<\/span> This performance is crucial for big data analytics, machine learning training, and other heavy workloads that require rapid access to massive datasets. Performance can be further enhanced by employing best practices such as using parallel requests to read or write data and leveraging multipart uploads to split large files into smaller parts that can be uploaded concurrently.<\/span>1<\/span><\/p>\n <\/p>\n
1.3 Strategic Cost Management: A Deep Dive into S3 Storage Classes and Lifecycle Policies<\/b><\/h3>\n
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A key advantage of S3 is its cost-effectiveness, which stems from a pay-as-you-go pricing model that eliminates the need for large, upfront capital expenditures on storage infrastructure.<\/span>1<\/span> Costs are incurred based on storage volume, the number and type of requests, and data transfer out of the AWS region. To help organizations optimize these costs, S3 offers a range of storage classes, each designed for a specific data access pattern.<\/span>2<\/span><\/p>\n <\/p>\n
Storage Tiers<\/b><\/h4>\n
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The primary storage classes are tiered based on access frequency and retrieval time:<\/span><\/p>\n\n- S3 Standard:<\/b> The default tier, designed for frequently accessed data that requires millisecond latency. It is ideal for dynamic websites, content distribution, and analytics workloads.<\/span>1<\/span><\/li>\n
- S3 Intelligent-Tiering:<\/b> This class is designed for data with unknown or fluctuating access patterns. It automatically moves objects between a frequent access tier and an infrequent access tier based on monitoring, optimizing costs without performance impact or operational overhead.<\/span>3<\/span><\/li>\n
- S3 Standard-Infrequent Access (S3 Standard-IA):<\/b> Suited for long-lived data that is accessed less frequently but requires rapid access when needed. It offers a lower storage price than S3 Standard but charges a per-GB retrieval fee.<\/span>3<\/span><\/li>\n
- S3 Glacier Storage Classes (Instant Retrieval, Flexible Retrieval, Deep Archive):<\/b> These are designed for long-term data archiving at the lowest possible cost. They offer the same 11 nines of durability but with different retrieval times and costs, ranging from milliseconds for Glacier Instant Retrieval to hours for Glacier Deep Archive.<\/span>1<\/span><\/li>\n<\/ul>\n
<\/p>\n
Lifecycle Policies<\/b><\/h4>\n
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To automate cost savings, S3 provides lifecycle policies. These are rules that can be configured to automatically transition objects between storage classes based on their age. For example, a policy could be set to move log files from S3 Standard to S3 Standard-IA after 30 days, and then to S3 Glacier Deep Archive after 180 days for long-term retention.<\/span>3<\/span> This automation is a critical component of managing the cost of a large-scale data lake over time.<\/span><\/p>\n <\/p>\n
1.4 S3 as the De Facto Standard for Modern Data Lakes<\/b><\/h3>\n
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The combination of virtually unlimited scalability, extreme durability, comprehensive security, and a tiered, cost-effective pricing model has made S3 the ideal foundation for modern data lakes.<\/span>2<\/span> A data lake is a centralized repository that allows for the storage of all structured and unstructured data at any scale.<\/span>2<\/span> S3’s ability to store data in its native format, from CSV and JSON files to Parquet and raw images, makes it perfectly suited for this paradigm.<\/span>3<\/span><\/p>\nA significant driver of S3’s dominance is its tight integration with the broader AWS analytics and machine learning ecosystem. Services like AWS Glue can be used for extract, transform, and load (ETL) jobs on data in S3; Amazon Athena allows for serverless, interactive SQL querying directly on S3 data; and Amazon SageMaker can pull training data from and store model artifacts in S3.<\/span>6<\/span> This seamless interoperability creates a powerful, cohesive platform for deriving value from data.<\/span><\/p>\nReal-world examples illustrate the power of S3-based data lakes. Siemens’ Cyber Defense Center collects 6 TB of log data per day into an S3 data lake for forensic analysis.<\/span>8<\/span> Georgia-Pacific streams real-time data from manufacturing equipment into S3 to optimize processes, saving millions annually.<\/span>8<\/span> Fanatics, a global e-commerce company, built its data lake on S3 to analyze huge volumes of transactional and back-office data, making it immediately available to its data science teams.<\/span>8<\/span> These cases underscore S3’s role not just as a storage repository, but as an active, central component of modern data strategy.<\/span><\/p>\n <\/p>\n
II. Versioning at Scale: Reproducibility with Data Version Control (DVC)<\/b><\/h2>\n
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While Amazon S3 provides a scalable foundation for storing data, it does not inherently offer a mechanism for versioning datasets and machine learning models in a way that aligns with software development best practices. This creates a critical gap in MLOps, where reproducibility is paramount. Data Version Control (DVC) is an open-source tool designed specifically to bridge this gap, extending the familiar principles of Git to the world of large-scale data and model management.<\/span><\/p>\n <\/p>\n
2.1 Bridging the Gap: Applying Git Principles to Data and Models<\/b><\/h3>\n
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The core challenge that DVC addresses is the impedance mismatch between traditional version control systems and the artifacts of machine learning.<\/span><\/p>\n <\/p>\n
The Core Problem<\/b><\/h4>\n
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Git, the standard for source code versioning, is optimized for managing small, text-based files. Its performance degrades significantly when large binary files, such as multi-gigabyte datasets or trained model weights, are committed directly to its history. This forces teams into ad-hoc solutions like storing data on shared drives or using complex file naming conventions (e.g., dataset_v2_final_final.csv), which are brittle and error-prone.<\/span>9<\/span> This practice severs the atomic link between the version of the code that produced a result and the specific version of the data it used, making experiments difficult to reproduce.<\/span>10<\/span><\/p>\n <\/p>\n
DVC’s Philosophy<\/b><\/h4>\n
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DVC’s philosophy is not to replace Git but to augment it.<\/span>9<\/span> It allows data science and machine learning teams to continue using the Git workflows they already know\u2014commits, branches, pull requests, and tags\u2014to manage the entire lifecycle of their projects.<\/span>11<\/span> DVC achieves this by codifying all aspects of an ML project, including data versions, models, and processing pipelines, into human-readable metafiles that are small enough to be efficiently managed by Git.<\/span>11<\/span> This approach elegantly decouples the versioning logic from the storage implementation, allowing each system to excel at its intended purpose. Git manages the complex versioning graph of small text files (code and metafiles), while a scalable object store like S3 handles the durable storage of large binary objects. This abstraction provides immense architectural flexibility, as the underlying storage backend can be changed simply by updating a configuration file, without altering the project’s Git history.<\/span>11<\/span><\/p>\n <\/p>\n
2.2 The DVC Architecture: Metafiles, Caching, and Remote Storage<\/b><\/h3>\n
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DVC’s architecture is based on a clever separation of concerns, which allows it to handle large files without bloating the Git repository.<\/span><\/p>\n <\/p>\n
Separation of Concerns<\/b><\/h4>\n
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The key architectural principle of DVC is to store small, lightweight pointer files, known as metafiles, within the Git repository, while the actual large data files are stored externally.<\/span>11<\/span> This keeps the Git repository small and fast, while still maintaining a verifiable link to the data.<\/span><\/p>\n <\/p>\n
The .dvc Metafile<\/b><\/h4>\n
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When a file or directory is added to DVC’s tracking using the dvc add command, DVC creates a corresponding .dvc metafile. This small, plain-text file contains metadata about the tracked data, most importantly a hash (typically MD5) that is calculated from the content of the data file(s).<\/span>14<\/span> This hash serves as a unique, verifiable fingerprint of the data’s state. The .dvc file is then committed to Git, acting as a stand-in for the actual data.<\/span>13<\/span><\/p>\n <\/p>\n
The DVC Cache<\/b><\/h4>\n
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The actual data files are stored in a local cache, typically located at .dvc\/cache within the project directory. This cache is a content-addressable storage system, meaning that files are organized based on their content hash.<\/span>9<\/span> If two different files in a project have the exact same content, only one copy is stored in the cache, providing automatic de-duplication.<\/span>12<\/span> This design is highly efficient for both storage and performance.<\/span><\/p>\n <\/p>\n
Remote Storage<\/b><\/h4>\n
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While the local cache is useful for a single user, collaboration requires a shared, centralized location for the data. DVC introduces the concept of a “remote” for this purpose. A DVC remote is a configurable backend where the contents of the cache can be pushed to and pulled from. This enables team members to share datasets and models easily.<\/span>16<\/span> DVC supports a wide variety of remote storage types, including cloud object stores like Amazon S3, Google Cloud Storage, and Azure Blob Storage, as well as SSH servers and network-attached storage.<\/span>9<\/span><\/p>\n <\/p>\n
2.3 Configuring S3 as a High-Performance DVC Backend<\/b><\/h3>\n
<\/p>\n
Amazon S3 is a natural and popular choice for a DVC remote due to its scalability, durability, and cost-effectiveness. Setting it up is a straightforward process.<\/span><\/p>\n <\/p>\n
Setup and Configuration<\/b><\/h4>\n
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The primary command for configuring a remote is dvc remote add. To set up an S3 bucket as the default remote, one would use a command like the following <\/span>13<\/span>:<\/span><\/p>\n <\/p>\n
Bash<\/span><\/p>\n <\/p>\n
$ dvc remote add -d myremote s3:\/\/my-dvc-bucket\/my-project<\/span><\/p>\n <\/p>\n
This command modifies the DVC configuration file (.dvc\/config), adding a section that points the remote named myremote to the specified S3 path and sets it as the default (-d) remote for dvc push and dvc pull operations.<\/span>16<\/span><\/p>\n <\/p>\n
Authentication and Permissions<\/b><\/h4>\n
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DVC seamlessly integrates with standard AWS authentication mechanisms. By default, it will use the credentials configured in the AWS CLI environment (e.g., via aws configure).<\/span>17<\/span> For more granular control or in environments where the CLI is not configured, credentials can be specified directly using dvc remote modify. It is a critical security best practice to store these sensitive credentials in a local, Git-ignored configuration file (.dvc\/config.local) to prevent them from being committed to the source code repository.<\/span>17<\/span><\/p>\nThe IAM user or role used by DVC requires a specific set of permissions on the S3 bucket to function correctly <\/span>17<\/span>:<\/span><\/p>\n\n- s3:ListBucket: To list objects in the remote.<\/span><\/li>\n
- s3:GetObject: To download files during a dvc pull.<\/span><\/li>\n
- s3:PutObject: To upload files during a dvc push.<\/span><\/li>\n
- s3:DeleteObject: For garbage collection operations.<\/span><\/li>\n<\/ul>\n
<\/p>\n
The Workflow<\/b><\/h4>\n
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The standard DVC workflow mirrors the Git workflow, making it intuitive for developers:<\/span><\/p>\n\n- Track Data:<\/b> A user adds a new dataset or model to be tracked with dvc add data\/my_dataset.csv. This creates the data\/my_dataset.csv.dvc metafile and adds the actual data to the local cache.<\/span>13<\/span><\/li>\n
- Commit Metafile:<\/b> The user commits the small .dvc metafile to Git: git add data\/my_dataset.csv.dvc followed by git commit -m “Add new version of dataset”. This records the data’s version in the project’s history.<\/span>13<\/span><\/li>\n
- Push Data:<\/b> The user uploads the data from their local cache to the shared S3 remote with dvc push. This command uses the content hash to check if the data already exists in the remote, avoiding redundant uploads.<\/span>14<\/span><\/li>\n<\/ol>\n
This workflow generates a specific profile of API requests on the S3 bucket. A dvc push translates into s3:PutObject requests for each new file, while a dvc pull results in s3:GetObject requests. For projects with thousands of small files, this can lead to a high volume of API requests, a factor that must be considered in cost modeling.<\/span>20<\/span><\/p>\n <\/p>\n
2.4 Collaborative MLOps: Enhancing Reproducibility and Team Synergy<\/b><\/h3>\n
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The integration of DVC with Git and a shared S3 backend provides a powerful framework for collaborative and reproducible machine learning.<\/span><\/p>\n <\/p>\n
Reproducibility<\/b><\/h4>\n
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By combining a Git commit hash with DVC’s data tracking, any historical state of a project can be perfectly recreated. A user can check out a past Git commit and run dvc pull. This command reads the .dvc metafiles from that commit and downloads the corresponding data versions from the S3 remote, restoring the exact state of the code, data, and models used for a particular experiment.<\/span>9<\/span> This ability to reliably reproduce results is the cornerstone of scientific rigor in machine learning.<\/span><\/p>\n <\/p>\n
Collaboration<\/b><\/h4>\n
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The centralized S3 remote acts as a hub for team collaboration. A data scientist can train a new model, run dvc push to upload it, and create a pull request in Git. A colleague can then review the code, check out the branch, and run dvc pull to download the exact model and data for validation or further experimentation.<\/span>9<\/span> This eliminates the need for manual file transfers and ensures that all team members are working with consistent and versioned artifacts.<\/span><\/p>\n <\/p>\n
Pipelines<\/b><\/h4>\n
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DVC extends beyond simple data versioning with its pipeline feature. Using a dvc.yaml file, users can define a Directed Acyclic Graph (DAG) of processing stages. Each stage connects a piece of code with its data dependencies and outputs.<\/span>13<\/span> For example, a pipeline could define stages for data preprocessing, model training, and evaluation. When dvc repro is run, DVC automatically determines which stages need to be re-executed based on changes to code or data, providing an efficient and reproducible way to manage complex ML workflows.<\/span>9<\/span><\/p>\n <\/p>\n
III. Building the Lakehouse: Reliability and Performance with Delta Lake<\/b><\/h2>\n
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While S3 provides a scalable storage foundation and DVC ensures reproducibility, raw data lakes built on object storage alone often suffer from reliability and performance issues. They can devolve into “data swamps” where data quality is poor, concurrent access is problematic, and performance degrades over time.<\/span>24<\/span> Delta Lake is an open-source storage layer that addresses these challenges by bringing the reliability, performance, and governance features of a traditional data warehouse to the data lake, creating a new architectural paradigm known as the “lakehouse.”<\/span><\/p>\n <\/p>\n
3.1 Transforming Data Lakes into Data Lakehouses<\/b><\/h3>\n
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The limitations of first-generation data lakes gave rise to the need for a more robust solution that could support a wider range of workloads, from ETL and BI to data science and machine learning.<\/span><\/p>\n <\/p>\n
The “Data Swamp” Problem<\/b><\/h4>\n
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Traditional data lakes, often consisting of vast collections of Parquet or CSV files on S3, lack critical database features. They do not support ACID transactions, meaning that concurrent read and write operations can lead to inconsistent or corrupted data. A job failure midway through writing could leave the dataset in a partial, unusable state. Furthermore, they lack schema enforcement, allowing malformed data to pollute the lake, and they offer no efficient way to perform record-level updates or deletes.<\/span>24<\/span><\/p>\n <\/p>\n
The Lakehouse Vision<\/b><\/h4>\n
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The lakehouse architecture, enabled by formats like Delta Lake, seeks to combine the best of both worlds: the low-cost, flexible, and scalable storage of a data lake with the performance, reliability, and data management features of a data warehouse.<\/span>26<\/span> By implementing a transactional metadata layer on top of standard open file formats in cloud object storage, Delta Lake allows organizations to build a single, unified platform for all their data workloads, eliminating the need for separate, siloed systems.<\/span><\/p>\n <\/p>\n
3.2 The Core of Delta Lake: Parquet Files and the Transaction Log<\/b><\/h3>\n
<\/p>\n
Delta Lake’s architecture is elegantly simple, building its powerful features on top of two core components stored directly in S3.<\/span><\/p>\n <\/p>\n
Data Files<\/b><\/h4>\n
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At its base, a Delta Lake table stores its data in the Apache Parquet format.<\/span>27<\/span> Parquet is an open-source, columnar storage format optimized for analytical query performance. Its columnar nature allows query engines to read only the specific columns needed for a query, dramatically reducing I\/O and improving speed compared to row-oriented formats like CSV.<\/span><\/p>\n <\/p>\n
The Transaction Log (_delta_log)<\/b><\/h4>\n
<\/p>\n
The true innovation of Delta Lake lies in its transaction log. Stored in a subdirectory named _delta_log alongside the Parquet data files, this log is the definitive source of truth for the table’s state.<\/span>
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