Navigating the complexities of ETL and analytics modernization: Challenges and proven strategies for success - LeapLogic
04 Mar 2024

Navigating the complexities of ETL and analytics modernization: Challenges and proven strategies for success

For data-driven enterprises of tomorrow, the imperative to modernize Extract, Transform, Load (ETL), and analytics workloads has become more evident than ever. As organizations grapple with the limitations of legacy stacks, their deeply inherited complex workloads often hinder the path to unlocking their business potential.

In this blog post, we will explore the multifaceted challenges encountered by enterprises in modernizing their ETL and analytics workloads. More importantly, we will unveil proven strategies and solutions that pave the way for successful ETL and analytics modernization.

Challenges with migrating legacy ETL workloads

While modernizing legacy ETL workloads can help enterprises advance their data goals, it can also bring forth several challenges that demand meticulous attention and strategic navigation. As enterprises embark on this transformative journey, several vital hurdles emerge, each presenting a unique set of complexities that require thoughtful consideration.

Some of these complex ETL modernization challenges are as follows:

1. Modernizing legacy ETL processing

Traditional processing within legacy ETL tools primarily involves in-memory operations and point-to-point data movement for ingress, transformation, and egress. In contrast, modern architectures such as data hubs, data lakes, hub-and-spoke, and medallion architectures advocate for an Extract, Load, and Transform (ELT) approach. This shift is propelled by the inherent advantages of modern on-demand cloud computing platforms, where distributed processing can be seamlessly harnessed. Consequently, the need to re-architect legacy ETL processes poses a significant challenge when transitioning to modern platforms.

2. Complex transformations and custom code

The intricacies of handling complex transformations, such as the normalization process, create a web of challenges. Managing aspects like schema variations, data volume, and the integration of diverse transformation logic demands careful maneuvering.

For instance, handwritten custom code in Informatica expressions introduces a layer of complexity. Ranging from simple code snippets to intricate algorithms, the need for transformation into target-native equivalents poses scalability issues, knowledge dependencies, and potential pitfalls in code consistency.

3. Dependency identification and analysis

One of the formidable challenges lies in accurately identifying interdependencies between different ETL workloads, particularly that of Informatica workflows and mappings and DataStage sequence and parallel jobs. The intricate relationships between Informatica workflows, sessions, and mappings necessitate an exhaustive understanding of end-to-end data and process lineage.

A missed interdependency can have cascading effects, leading to inaccurate scope creep, extended migration effort, and adversely affecting business timelines and outcomes. That’s why a thorough (and automated) approach to logically grouping workloads for migration becomes quintessential.

4. Parameter file handling

The intrinsic role of parameter files in any cloud-native migration adds another layer of complexity. For example, with Informatica XMLs come parameter files that typically contain session and workflow parameters, workflow and session variables, database connection info, file paths and directory locations, environment-specific details, session configurations, runtime properties, and more. Ensuring the seamless transition of parameter files becomes crucial for maintaining the integrity and functionality of ETL workloads in the target environment.

5. Varied file types and formats

The diversity in file types, such as DSX or XML or others, including fixed-width or binary files, introduces challenges that migration tools must adeptly handle. The fixed width files could have issues related to column alignment, schema definition, lack of delimiters, record length consistency, and encoding. On the other hand, binary files lack human readability, data alignment, data types, header and footer interpretation, variable-length fields, etc.

6. Unstructured data sources

The extraction of data from unstructured sources is a daunting task. Tackling issues related to heterogeneity, data quality, semantic ambiguity, and integrating unstructured data with structured sources demands robust native capabilities.

7. Mapplets conversion

The automatic conversion of mapplets, often employed as reusable components in legacy ETL systems like Informatica, poses significant challenges. Balancing considerations such as dependencies, scalability, and the limited debugging options inherent in these components requires a nuanced approach.

Challenges with migrating legacy analytics workloads

When it comes to migrating legacy analytics workloads from sources like SAS, several unforeseen and difficult challenges are encountered. As organizations venture into the modernization of analytics processes, they encounter the following major obstacles:

1. Traditional on-premises single machine/grid processing

Over the last few years, SAS analytics has predominantly operated within the confines of single-server setups or, more recently, within SAS grids enabling parallelism, albeit within the constraints of limited grid node sizes. These computational limitations significantly impede large-scale analytics, a crucial necessity in the modern landscape dominated by big data-driven models. Thus, achieving scalability and enhancing performance emerges as a pivotal challenge during the migration of SAS workloads to modern platforms. This entails harnessing the potential of big data to amplify analytical processing capabilities.

2. Auto-conversion of dynamic Proc SQLs

Dynamic query creation, reliant on conditions not natively supported in target systems, complicates workload migration. Handling this intricacy demands careful consideration and strategic conversion processes. For instance, you can consider converting them to open collaborative-based languages such as Python, which are easy to maintain and supports advanced features.

3. Range and wildcard variables

Variables created using another variable in the name, employed for code simplification, efficiency, and dynamic adaptations, introduce ambiguity during migration. The challenge lies in treating and converting these variables appropriately within the global context.

4. Data block with merge/union

For SAS workloads, Union and Merge operations within data blocks, encompassing large datasets with diverse data types and formats, present a formidable challenge. Ensuring the meticulous handling of complex transformations and filtering conditions is imperative.

5. Macro blocks

Addressing the intricacies of handling macros in the target environment, where native support may be lacking. Macro blocks often involve variables, resolution, parameterization, looping, debugging challenges, conditional processing, dynamic code generation, and integration with data steps and procedures.

6. Various PROCS such as Proc Transpose

Handling SAS-specific procedures (like Proc Transpose), which may not be natively available in target environments, can be tedious. For example, you can encounter challenges related to BY-Group processing, handling missing values, sparse data, variable order, variable formats, multiple transpositions, and managing long vs. wide formats.

7. Varied file formats

The handling and conversion of diverse file formats, including SAS and EGP, add complexity to the migration process. While various file formats differ in purpose, content, and usage, choosing the appropriate format based on specific use cases and requirements introduces an additional layer of intricacy.

Simplify the transformation of complex workloads with end-to-end automation

LeapLogic’s end-to-end automated transformation capabilities provide robust solutions to overcome unwanted complexities for modernizing legacy data warehouse, ETL, analytics, Hadoop, and BI workloads.

Auto-transformation of code

LeapLogic leverages the power of end-to-end automation to transform intricate code pieces, dialects, and constructs from legacy workloads to a modern cloud-native stack. Whether it’s dealing with complex transformations, user-written custom code, or intricate dependencies, LeapLogic efficiently tackles each aspect, ensuring a smooth and optimized cut-over.

Efficient handling of super-complex code patterns

LeapLogic is designed to handle a spectrum of use cases, functions, procedural and conditional statements, and corner cases with unparalleled efficiency. Leaplogic’s intelligent transformation engine and maturity gained through several migration projects ensure that even the most intricate aspects of workload migration are seamlessly addressed.

Intelligent grammar-based engine

At the heart of LeapLogic’s capabilities lies an intelligent grammar-based code transformation engine, finely tuned through decades of migration experiences. The engine is equipped with the insights gained from numerous successful migrations, boasting a deep understanding of various legacy sources and targets, incorporating expertise in technical debt reduction and inherent performance tuning and optimizations.

Modernization to state-of-the-art architectures

LeapLogic auto-transforms legacy ETL and SAS analytical architectures into modern frameworks, particularly cloud-based on-demand elastic architectures. This aims to optimize the cost-to-performance ratio while harnessing the potential of big data. LeapLogic facilitates automatic conversion of existing code bases into cloud-friendly parallel and distributed programs, tailored to align with customer architectures and adhere to cloud best practices. By seamlessly transitioning enterprises to modern architectures, LeapLogic not only paves the way for enhanced efficiency but also lays the groundwork for the future of GenAI and machine learning (ML) within these organizations.

Proven track-record

LeapLogic has delivered successful transformation outcomes for leading Fortune 500 enterprises across diverse industries for over a decade. Using cutting-edge technology and innovation, LeapLogic has consistently demonstrated its ability to address customized use cases and enterprise-grade scenarios.


LeapLogic’s 4-step approach ensures a smooth, end-to-end modernization journey – starting with a comprehensive assessment, followed by automated transformation, validation, and all the way up to operationalization.


Step 1

Complete analysis of workloads, code profiling, and dependencies with actionable recommendations

Step 2

End-to-end transformation, including core business logic to target-native equivalents

Step 3

Validation for pipelines, data, and row and cell-level queries

Step 4

Target-specific executable packaging with optimal price-performance ratio

Step 1

Complete analysis of workloads, code profiling, and dependencies with actionable recommendations

Step 2

End-to-end transformation, including core business logic to target-native equivalents

Step 3

Validation for pipelines, data, and row- and cell-level queries

Step 4

Target-specific executable packaging with optimal price-performance ratio

Bottom line

While legacy ETL and analytics workloads come up with several challenges, a modern automation-powered tool like LeapLogic helps overcome them by taking care of all possible corner cases. In essence, LeapLogic offers a comprehensive solution that not only addresses the challenges posed by legacy workload migration but also propels enterprises toward a future where data management, analytics, and BI capabilities seamlessly align with the demands of the modern era. Furthermore, modern cloud-native systems provide a competitive edge to enterprises, helping them become Gen AI-ready and unlock the true potential of their business.

Gurvinder Arora
Gurvinder Arora
Senior Lead Technical Writer