(Part 2/3) Rethinking ETLs - How Large Language Models (LLM) can enhance Data Transformation and Integration

Part 2: Exploring examples and optimization goals

In the second installment of our three-part series on rethinking ETL processes through the lens of Large Language Models (LLMs), we shift our focus from the search for an optimal algorithm, covered in Part 1, to exploring practical examples and defining clear optimization goals.

Large Language Models have proven their potential in streamlining complex computational tasks, and their integration into ETL workflows promises to revolutionize how data is transformed and integrated.

Today, we will delve into specific examples that will form the building blocks of LLMs’ role in various stages of the ETL pipeline — from extracting data from diverse sources, transforming it for enhanced analysis, to efficiently loading it into final destinations. We will also outline key optimization goals designed to enhance efficiency, accuracy, and scalability within ETL processes. These goals will form target goals for out LLM Agents in the ETL Workflow design and optimization in Part 3.

Let’s start with some examples.


Example 1: Simplified ETL

Consider a simplified ETL scenario where you have:

  • Input Dataset: A large sales transactions table.
  • Output Dataset: A summarized report with sales aggregated by region and month.
  • Available Operations:
    • Filter (remove unwanted transactions)
    • Group By (region, month)
    • Aggregate (calculate sum of sales)
    • Sort (order the output by region and month)

Cost Modeling We’ll assume the primary cost factor is the size of the dataset at each stage:

  • Operations that reduce dataset size have lower costs.
  • Operations that maintain or increase size have higher costs.

Heuristic Function

  • h(n): Estimates the cost to reach the goal (output dataset) from node n
  • Our heuristic could be the estimated difference in the number of rows between the dataset at node ‘n’ and the expected number of rows in the final output.

A* Search in Action

  1. Start: Begin at the input dataset node.
  2. Expansion: Consider possible operations (filter, group by, etc.).
    • Calculate the actual cost g(n) of reaching the new node.
    • Estimate the heuristic cost h(n) for the new node.
    • Add nodes to a priority queue ordered by f(n) = g(n) + h(n).
  3. Prioritization: The A* algorithm will favor exploring nodes with the lowest estimated total cost (f(n)).

  4. Path Discovery: Continue expanding nodes until the output dataset node is reached.



Example Decision

  • Assume ‘filtering’ reduces dataset size significantly with a low cost.
  • ‘Group by’ and ‘aggregate’ reduce size but have moderate costs.
  • ‘Sort’ has a cost but doesn’t change the dataset size.

A* might prioritize an ETL path with early filtering, as the heuristic will indicate this gets us closer (in terms of data size) to the final output structure more quickly.



A More Complex Scenario

Setup

  1. Input Datasets

    • Large customer data file (CSV) with potential quality issues.
    • Product reference table (database table).
    • Web clickstream logs (semi-structured JSON).
  2. Output Dataset

    • A well-structured, normalized table in a data warehouse, suitable for sales trend analysis by product category, customer demographics, and time period.
  3. Available Operations

    • Data cleaning: Fixing malformed data, handling missing values (various imputation techniques).
    • Filtering: Removing irrelevant records.
    • Parsing: Extracting information from JSON logs.
    • Joining: Combining customer data, product data, and clickstream events.
    • Normalization: Restructuring data into appropriate tables.
    • Aggregation: Calculating sales amounts, event counts, etc., at various granularities (daily, weekly, by product category).
  4. Cost Factors

    • Computational Complexity: Certain joins, complex aggregations, and advanced data cleaning are costly.
    • Data Volume: Impacts processing and storage at each step.
    • Development Time: Custom parsing or intricate cleaning logic might have high development costs.
    • Error Potential: Operations prone to error (e.g., complex parsing) carry the risk of rework.
  5. Heuristic Function Possibilities

    • Schema Similarity: Estimate how close a dataset’s structure is to the final schema (number of matching fields, normalization needs).
    • Data Reduction: Favor operations that significantly reduce dataset size early in the process.
    • Dependency Alignment: If certain output fields depend on others, prioritize operations that generate those dependencies first.



A* in Action

The A* search would traverse a complex graph. Decisions could include:

  • Cleaning vs. Filtering: If data quality is very poor, A* might favor cleaning operations upfront, even if they don’t reduce size considerably, because bad data could cause costlier problems downstream.
  • Parse First vs. Join First: The heuristic might guide whether to parse clickstream data or join with reference tables, depending on estimated output size and downstream dependencies.
  • Aggregation Granularity: Determine when to do preliminary aggregations guided by the heuristic, balancing early data reduction with the need to retain data for the final output granularity.
Benefits of A* in this Complex ETL Scenario
  • Adaptability: A* can handle diverse cost factors and optimization goals by adjusting cost models and heuristics.
  • Pruning: A good heuristic can help avoid exploring unpromising ETL paths, saving computational resources.
  • Evolution: You can start with basic heuristics and refine them as you learn more about the actual performance of our ETL process.
Caveats
  • Heuristic Design: Designing effective heuristics in intricate ETL scenarios is challenging and requires domain knowledge about the data and operations.
  • Overhead: A* itself has some computational overhead compared to a simpler algorithm like Dijkstra’s.




Heuristics Design Strategy

We can consider different heuristic approaches when designing our A* search for ETL optimization, along with the types of domain knowledge they leverage:

Heuristic Types

  1. Schema-Based Similarity

    • Logic: Measures how close the dataset at a given node is to the structure of the final output schema.
    • Domain Knowledge: Requires understanding the desired target schema fields, relationships, and normalization requirements.
    • Example: Count matching fields, penalize the need for normalization or complex restructuring.
  2. Data Volume Reduction

    • Logic: Favors operations that significantly reduce dataset size (in terms of rows or overall data).
    • Domain Knowledge: Understanding which operations tend to reduce data size (e.g., filtering, aggregations with appropriate grouping).
    • Example: Estimate the percentage of data likely to be removed by a filtering operation.
  3. Dependency Resolution

    • Logic: Prioritizes operations that generate fields or datasets needed for downstream transformations.
    • Domain Knowledge: Understanding the dependencies between different output fields and how operations create them.
    • Example: If a field in the output depends on joining two datasets, favor the join operation early if it leads to lower overall costs.
  4. Error Risk Mitigation

    • Logic: Penalizes paths that include operations with a high potential for errors or that propagate errors from earlier stages.
    • Domain Knowledge: Understanding data quality issues, common failure points of operations (e.g., parsing complex data), and the impact of errors on costs (rework, etc.).
    • Example: Increase the estimated cost of joins on fields that are known to have potential mismatches.
  5. Computational Complexity Awareness

    • Logic: Factor in the known computational intensity of different operations.
    • Domain Knowledge: Understanding which operations are generally CPU-bound, memory-bound, or have I/O bottlenecks.
    • Example: Slightly penalize computationally expensive joins or complex aggregations.
Hybrid Heuristics

In complex ETL scenarios, you’ll likely get the best results by combining aspects of these heuristics. For instance: Prioritize early filtering to reduce data size, BUT check if it depends on fields that need cleaning first. Favor a computationally expensive join if it’s essential for generating multiple output fields and avoids several smaller joins later.



Building a Heuristic Strategy

Consider the ETL operation in Banking, where we are building the Customer 360 degree view. The Data sources are the customer transactions from POS with Credit Card numbers need to be hashed before joining with the customer profile. Third Party datasets are also used to augment the customer profile, which are only available end of day. Datasets also include recent call center interaction view and past Campaigns /and offers prepared for the customer.



Optimization Goal #1

Dependency Resolution

Concept Developement

Let’s design a heuristic specifically tailored for dependency resolution as our optimization goal.

Understanding the Scenario

  • Core Dependency: It seems like the hashed credit card number is a crucial linking field to join the transaction data with the customer profile.
  • Temporal Dependency: Third-party data augmentation can only happen once it’s available at the end of the day.
  • Potential for Parallelism: The call center interaction view and the campaign/offer history likely don’t directly depend on the core customer profile join.

Dependency Resolution Heuristic

Our heuristic h(n) should estimate the cost to reach the final output dataset from node n. Here’s a possible approach:

  1. Critical Path: Identify the operations required to join the transaction data with the customer profile (e.g., hashing, potentially cleaning, the join itself). Assign a high priority to nodes along this path.
  2. Blocking Dependencies: If a node represents a state where certain datasets remain unjoined, increase the heuristic cost proportionally to the number of output fields still dependent on those joins.
  3. End-of-Day Bottleneck: Introduce a time dependency factor. While the third-party augmentation is delayed, artificially increase the cost of nodes requiring that data, effectively postponing those operations in the search.
  4. Parallelism Bonus: Slightly decrease the heuristic cost for nodes representing datasets involved in the call center view or campaign history since those could potentially be processed in parallel with the core dependency chain.
Execution Planning
  • Node A: Transaction data hashed, Customer Profile ready, but not yet joined. This node would likely have a high heuristic cost due to the blocking dependency.
  • Node B: Represents the call center interaction view partially prepared. This node might have a slightly lower heuristic cost due to the parallelism bonus.

Domain Knowledge Required

Linking Fields: Precisely which fields form the basis for joins. Typical Data Volumes: Understanding which joins might be computationally more expensive due to dataset sizes.

Refinement

Although this heuristic is a good starting point, it can be further refined.

  • Learning from Execution: If certain joins consistently take longer, increase their cost contribution within the heuristic.
  • Factoring in Error Potential: If specific datasets are prone to quality issues delaying downstream processes, include this risk in the heuristic estimation.

Optimization Goal #2

Resource Usage Minimization

Concept Developement

Here’s a breakdown of factors we could incorporate into a heuristic h(n) that estimates the resource usage impact from a given node n onwards:

  1. Dataset Size Anticipation:

    • Expansive Operations: Penalize operations likely to increase dataset size significantly (e.g., certain joins, unnest operations on complex data).
    • Reductive Operations: Favor operations known to reduce dataset size (filtering, aggregation with ‘lossy’ calculations like averages).
    • Estimation: You might need some profiling of our datasets to understand the average impact of different operations.
  2. Memory-Intensive Operations: Identify operations likely to require large in-memory processing (complex sorts, joins with certain algorithms). Increase the cost contribution of nodes leading to those operations.

  3. Network Bottlenecks: If data movement is a concern, factor in operations that involve transferring large datasets between systems. Increase the cost contribution for nodes where this movement is necessary.

  4. Temporary Storage:

If some operations necessitate intermediate storage, include an estimate of the storage cost in the heuristic calculation.


Execution Planning

Effective execution planning is key to optimizing performance and managing resources. Our approach involves dissecting the workflow into distinct nodes, each with unique characteristics and challenges. Let’s delve into the specifics of two critical nodes in our current pipeline, examining their roles and the anticipated heuristic costs associated with their operations.

  • Node A: Represents a state after filtering transactions down to a specific time period (reducing size) followed by a memory-intensive sort. The heuristic cost might be moderate (reduction bonus, but sort penalty).

  • Node B: Represents a state where a large external dataset needs to be joined, likely increasing dataset size and potentially involving data transfer. This node would likely have a higher heuristic cost.


Mathematical Representions

Node A

To represent Node A mathematically, we can describe it using notation that captures the operations and their effects on data size and processing cost. Here’s a conceptual mathematical representation:

Let’s define:

  • \(D\): Initial dataset.
  • \(t*{1}, t*{2}\): Time boundaries for filtering.
  • \(f(D, t*{1}, t*{2})\): Function that filters \(D\) to include only transactions within the time period \([t_{1}, t_{2}]\).
  • \(s(X)\): Function that sorts dataset \(X\) in memory.

Then, Node A can be represented as: \(A = s(f(D, t_1, t_2))\)

Here, \(f(D, t_1, t_2)\) reduces the size of \(D\) by filtering out transactions outside the specified time window, and \(s(X)\) represents a memory-intensive sorting operation on the filtered dataset. The overall cost \(C_A\) for Node A could be estimated by considering both the reduction in size (which decreases cost) and the sorting penalty (which increases cost). Mathematically, the cost might be represented as:

\[C_A = cost(f(D, t_1, t_2)) - reduction_bonus + cost(s(X)) + sort_penalty\]

This formula provides a way to quantify the heuristic cost of operations performed in Node A, taking into account both the benefits and penalties of the operations involved.


Node B

For Node B, which involves joining a large external dataset and possibly increases the dataset size and incurs data transfer costs, we can also set up a mathematical representation using appropriate functions and operations.

Let’s define:

  • \(D\): initial dataset
  • \(E\): large external dataset
  • \(j(D, E)\): Function that joins \(D\) with \(E\)

Node B can then be represented as: \(B = j(D, E)\)

Here, \(j(D, E)\) represents the join operation that combines dataset \(D\) with external dataset \(E\), likely increasing the size and complexity of the data.

Considering the resource costs, particularly for data transfer and increased dataset size, we can mathematically represent the cost \(C_B\) for Node B as follows:

\[C_B = base_cost(D) + base_cost(E) + join_cost(D, E) + data_transfer_cost + size_penalty\]
  • \(base_cost(D)\) and \(base_cost(E)\) represent the inherent costs of handling datasets \(D\) and \(E\), respectively.
  • \(join_cost(D, E)\) accounts for the computational overhead of performing the join operation.
  • \(data_transfer_cost\) covers the expenses related to transferring \(E\) if it is not locally available.
  • \(size_penalty\) is added due to the increased dataset size resulting from the join, which may affect subsequent processing steps.

This formulation provides a baseline framework to analyze the costs associated with Node B in your data processing pipeline.


Domain Knowledge Required
  • Operational Costs: Understand which specific operations in our ETL environment tend to be CPU-bound, memory-bound, or network-bound.
  • Data Sizes: Have a general sense of the relative sizes of our datasets and how those sizes might change after typical transformations.


Hybrid Approach

Crucially, we may want to combine this resource-focused heuristic with our earlier dependency resolution heuristic. Here’s how we could do this:

  • Weighted Sum: h(n) = weight_dependency * h_dependency(n) + weight_resource * h_resource(n). Experiment with weights to find a balance between our optimization goals.
  • Conditional Prioritization: Perhaps use h_dependency(n) as the primary guide, but if two paths have similar dependency costs, then use h_resource(n) as a tie-breaker.

Further refinements

As we continue to optimize our ETL processes, it’s crucial to consider how we can further enhance the efficiency and cost-effectiveness of our operations (beyond the hyrbid approaches discussed). There are several key areas where further refinements could prove beneficial. Let’s explore how targeted adjustments might help us manage resources better and smooth out any recurring bottlenecks in our processes.

  • Are there particular resources (CPU, memory, network, cloud storage) that are our primary cost concern? We could fine-tune the heuristic to be more sensitive to those.
  • Do we have any insights from past ETL executions about which operations consistently become resource bottlenecks?



In the final iteration, we will explore how to integrate Large Language Models (LLMs) as agents to enhance various aspects of the ETL optimization process we’ve been discussing.




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