Part 3 - Streamlining Data Pipelines for Efficiency

Part 3 of 5 in the Series "Navigating the Future of Analytics Modernization"

Efficiency lies at the heart of every successful endeavor. Part 3 shines a spotlight on Process Improvements, illuminating the pathways to streamlined operations, enhanced workflows, and optimized resource utilization. From reimagining data ingestion protocols to fortifying data quality initiatives, we explore the transformative potential of process optimization within Data Pipelines.

Introduction

Efficiency is the cornerstone of progress, driving innovation and success in the modern digital landscape. In this installment, we delve into the realm of Process Improvements within Data Pipelines, unlocking pathways to enhanced productivity and operational excellence. By scrutinizing data ingestion protocols and bolstering data quality initiatives, we harness the transformative power of process optimization to propel organizations towards their strategic goals.

Solution Improvements and Enhancements

To navigate the evolving digital terrain, it's imperative to adhere to high-level guiding principles that address both current pain points and future-focused digital aspirations. Embracing principles such as eliminating redundancy, enhancing analytic capabilities, optimizing processes, and ensuring data completeness lays the foundation for a robust and future-proof data platform. By prioritizing these principles, organizations can build solutions that not only address immediate challenges but also align with long-term strategic objectives.

These principles serve as the foundation upon which organizations build their data strategies, driving innovation, and informed decision-making. Let's delve into each guiding principle and its associated solution features to understand their significance in shaping a robust data infrastructure.

Guiding Principles:

1. Enable single point of access to data and ensure no duplication across environments
2. Enable business-driven processes not constrained by technical design
3. Enable point-in-time analysis for business users
4. Ensure critical data sets can be processed on priority
5. Ensure no data loss during data load process from multiple sources
6. Maintain high quality and accuracy through data quality checks
7. Raise awareness of the data through data science
8. Introduce new capabilities and environment for business users
9. Enable near real-time data processing for faster data availability
10. Enable streamlined data access for analytics purpose

Solution Features:

1. Consolidate regional data marts into a global data mart
2. Tagging and logical grouping of data by subject area
3. Process data in micro and standard batch
4. Capture surrogate keys and slowly changing dimensions (SCD) for point-in-time analysis
5. Load all data into the Data Lake for data completeness
6. Record and report records with referential integrity issues
7. Enhance reporting capabilities with Data Cataloging and Self-service analytics
8. Platform for advanced analytics and collaboration

Guiding principles form the bedrock of a successful data strategy, guiding organizations towards optimal data management practices. By establishing a centralized Data Lake, prioritizing business-driven processes, enabling point-in-time analysis, ensuring the prioritization of critical data sets, preventing data loss, maintaining data quality, leveraging data science, fostering user empowerment, enabling near real-time data processing, and facilitating streamlined data access, organizations can unlock the full potential of their data assets. These principles, when coupled with solution features such as consolidating regional data marts, implementing tagging and logical grouping, processing data efficiently, capturing key data elements accurately, ensuring data completeness, addressing integrity issues, enhancing reporting capabilities, and fostering collaboration, pave the way for data-driven success in today's dynamic business landscape.

The Future State Environment

The future state environment of data pipelines encompasses both micro and batch data processes, catering to diverse use cases and requirements. Micro data processes, characterized by more frequent data updates, offer agility and responsiveness to real-time insights. Meanwhile, Batch data processes handle larger volumes of data with scheduled processing, ensuring comprehensive analysis and reporting capabilities. By optimizing the data refresh process, leveraging intermediate files with delta records, and parallelizing processing layers, organizations can streamline data pipelines and expedite data delivery. Additionally, segregating current and historical data for targeted refreshes and parallelizing translate and curate layers further accelerates the critical path of the data pipeline, enhancing overall efficiency and agility. For an in depth assessment of ELT vs ETL see the LinkedIn article Data Integration: ELT Performance vs. ETL, Methods (With Tech Insights) .

Micro Batch Processing

Micro Batch processing involves processing data in small, frequent batches, typically on a near real-time basis. Here's a list of the points regarding Micro Batch processing further elaborated in this section:

1. RAW dataset collection and loading into translate layer
2. Types of data requiring near real-time processing
3. Input data processed at multiple intervals
4. Logical Data Pipeline stages
5. Capturing current snapshot through Micro Batch
6. Access Layer and Data Consumption

Micro Batch processing offers a balance between real-time responsiveness and processing efficiency, making it suitable for scenarios where data needs to be analyzed and acted upon quickly without sacrificing accuracy or scalability. By leveraging Micro Batch processing techniques, organizations can derive timely insights from their data, driving informed decision-making and competitive advantage in today's fast-paced business environment.

Batch processing is a fundamental approach within data pipelines, particularly suited for handling certain types of data that require end-of-day processing. Here's an elaboration on batch processing in the context of data pipelines, focusing on dimension data, transaction data, and aggregates:

Batch Processing

Batch processing refers to the method of processing data in large, discrete chunks or batches, typically at scheduled intervals, such as daily or nightly runs. This approach is particularly well-suited for handling data sets that do not require real-time processing and can be processed efficiently in bulk.

Types of Data for Daily Processing:

Within the tried-and-true batch processing paradigm, several types of data are still commonly processed in batch:

1. Dimension Data: Dimensional data, such as reference tables or lookup tables, often require end-of-day processing to ensure that they reflect the most up-to-date information. This includes data related to products, customers, locations, and other static attributes.
2. Transaction Data: Transactional data, such as sales transactions, financial transactions, or inventory movements, are typically processed at the end of the day to summarize daily activities and update aggregate metrics.
3. Aggregates: Aggregated data, which summarizes and consolidates information from multiple sources or at different levels of granularity, may also be processed at the end of the day to generate reports or analytics insights.

Input Data Processed Once Per Day: In modernized batch processing, input data is typically processed daily, following a predefined schedule or batch processing window. This allows for the efficient processing of large volumes of data within a specific time frame, without the need for real-time processing capabilities.

Logical Data Pipeline: Batch processing is an integral part of the logical data pipeline, which encompasses stages such as Translate, Curate, and Collect. In this pipeline, raw data is translated into a usable format, curated to ensure quality and consistency, and collected into a central repository for further analysis.

Latest Snapshot: At the end of the batch processing cycle, the pipeline generates the latest snapshot of processed data, reflecting the aggregated and summarized information for that period. This snapshot serves as the basis for reporting, analytics, and decision-making, providing stakeholders with insights into daily operations and performance metrics.?

Batch processing plays a critical role in data pipelines, enabling the efficient processing of large volumes of data at scheduled intervals. By processing dimension data, transaction data, and aggregates at the end of the day, batch processing ensures that stakeholders have access to accurate, up-to-date information for reporting and analysis purposes.

Anatomy of an Efficient Micro Batch Pipeline

Micro Batch Processing for Transactional Entities with Higher Data Volume involves optimizing the data refresh process to handle frequent updates efficiently. Here's a detailed breakdown of the steps involved:

Step 1: Collecting Smaller Change Datasets

• At regular intervals (every 15 minutes to 1 hour), smaller change datasets are collected from the source system (and ERP system for example) and stored in raw data format.
• The collected data feeds into the Collect layer, where it maintains both current and historical raw data in file storage for each cycle.

Step 2: Translation and Loading into Translate Layer

• The collected data is then processed in the Translate layer using a compute engine (e.g., Apache Spark Core) to transform and load it into a more usable format.
• Temporary files are generated containing only the change data, which will be used for subsequent processing.

Step 3: Identifying Partitions Requiring Updates

• A script is executed to identify partitions that require updates on the target files or database tables based on the change data.
• The result of this script categorizes files into those requiring updates (less than 4%) and those that do not (96% or greater).

Step 4: Merging Change Data into Main Files

• Once the temporary files are prepared, a compute engine (e.g., Apache Spark Core) is utilized to merge them into the main data files, touching only the impacted files.
• This merging process runs in parallel to Step 3, optimizing efficiency.

Step 5: Transformation and Loading into Curate Layer

• The processed data from the Translate layer is combined with additional transformation steps and loaded into the Curate layer using a compute engine.
• Hash keys are generated instead of surrogate keys to optimize the load process.
• Existing data from respective partition files is deleted, and only insert operations are performed on applicable partitions to avoid database-level computation.
• This step ensures that partitioned tables, which require less than 4% update, are handled efficiently.

By following these optimized steps, the Micro Batch Processing approach effectively handles frequent updates for transactional entities with higher data volume, ensuring timely and accurate data refreshes without compromising performance or scalability.

Optimizing Data Pipelines for Big Data: A Hybrid Approach of Micro and Standard Batch Processing

This approach involves a combination of Micro and Standard Batch Processing for managing master data efficiently. Here's an elaboration on each step:

Step 1 (Micro Batch - Collect Layer):

• Smaller change datasets are collected from the source system at frequent intervals (every 15 minutes to 1 hour).
• These datasets are stored in Raw Parquet format in the Collect Layer, maintaining both current and historical raw data in File Storage (e.g., AWS S3) for each cycle.

Step 2 (Micro Batch - Translate Layer):

• Utilizing a compute engine, the collected data is transformed and loaded into the Translate Layer.
• Change data is stored in a separate temporary file, preparing it for further processing.

Step 3 (Micro Batch - Merge Data):

• Once the temporary file is ready, a compute engine is used to merge it into the main data files.

• This process runs in parallel with Step 4 to optimize the critical path of the data pipeline.

Step 4 (Micro Batch - Curate Layer):

• Another compute engine combines data from the Translate Layer, performs transformations, and loads it into the Curate Layer.
• Only active data is retained in the Curate Layer, and hash keys are generated instead of surrogate keys to optimize the data load process.
• Existing data from the target table is deleted, and only insert operations are performed to avoid database-level computation.

Step 5a (Standard Batch - Translate Layer):

• Daily standard batch processing involves a one-time load by the end of the day to maintain a history of end-dated records.

• This data is loaded into the HIST folder in the Translate Layer. While this process is optional for most data entities, it is required for some selective entities.

Step 5b (Standard Batch - HIST Table):

• Similar to Step 5a, a one-time load by the end of the day is performed to maintain a history of end-dated records.

• However, this data is loaded into the HIST table, and it is required for selective entities.

By employing this hybrid approach of Micro and Standard Batch Processing, organizations can efficiently manage data, ensuring both real-time updates and historical tracking for informed decision-making and analysis.

See Driving Efficiency and Insights: The Intersection of Data Architecture, Analytics, and Logistics Optimization for examples of effective ETL/ELT data pipeline design patterns and various processing paradigms and technologies tailored to meet the unique demands of the smart transport and logistics industry.

Optimizing Data Processing and Storage: Solutions for Surrogate Keys, Referential Integrity, and Slowly Changing Dimensions

In pursuit of data completeness, availability, and accuracy, the optimization of data processing and storage mechanisms is imperative. To mitigate issues such as unnecessary overhead and maintain data integrity, targeted solutions are proposed for common challenges:

1. Surrogate Keys Optimization:

• Addressing the overhead of maintaining multiple surrogate keys and unnecessary joins, hash keys will be exclusively generated and maintained in the Curated layer.?
• This strategic approach aims to streamline reporting data preparation and minimize processing and storage burdens, with the Translate layer omitting both surrogate and hash keys.

2. Referential Integrity Enhancement:

• To streamline data reconciliation and processing, records failing Referential Integrity checks will not only be redirected to an error table but also loaded into both the translate and curate layers.?
• This facilitates easier identification and reprocessing of failed records post-source updates, ensuring data accuracy and completeness in reporting tables.

3. Slowly Changing Dimension Optimization:

• Addressing redundancy and overhead resulting from historical data maintenance, active and historical data will be maintained separately. Historical records will be selectively generated for business-critical attributes only, with the Translate or Curated layer designated for history maintenance.?
• This targeted approach minimizes process and data overhead while ensuring historical data integrity for essential business insights.

Conclusion

In conclusion, Part 3 underscores the importance of Process Improvements within Data Pipelines, outlining strategies to optimize operations, enhance workflows, and maximize resource utilization. By embracing a culture of continuous improvement and leveraging innovative techniques, organizations can drive meaningful transformations and achieve sustained success in the data-driven era.?

As we navigate the complexities of modernized data analytics, it becomes evident that our journey towards data excellence requires a strategic alignment of technologies, processes, and methodologies. By optimizing data processing and storage mechanisms and addressing common challenges such as surrogate keys, referential integrity, and slowly changing dimensions, organizations can achieve unparalleled levels of data completeness, availability, and accuracy.?

Bidding farewell to Part 3, we eagerly anticipate delving into the realm of Frameworks and Best Practices in our next installment. Part 4 promises to unveil a treasure trove of insights, guiding organizations towards analytics prowess and sustained success in today's dynamic data-driven landscape.

Part 4: Frameworks and Best Practices

Guided by a commitment to excellence, Part 4 unveils a treasure trove of Frameworks and Best Practices meticulously curated to propel organizations towards analytics prowess. From orchestrating seamless data workflows to upholding stringent audit, balance, and control mechanisms, we unveil the blueprint for sustained success in the data-driven landscape.