From driving digital innovation to streamlining operations, cloud-based systems have revolutionized the business landscape in the last decade. The increasing number of investments from small and medium enterprises has led to a considerable rise in cloud adoption. In 2020, the cloud migration services market was valued at USD 119.13 billion. This number is expected to grow up to USD 448.34 billion by 2026. Considering the significant reduction in IT expenses, cloud adoption has become a global phenomenon among businesses.
However, despite having the cloud as the critical enabler for greater organizational agility and growth, successfully mobilizing data for agile and effective decision-making has been a critical challenge for many data-aware businesses. The phenomenal increase in the volumes, variety, velocity, and value of data in recent years has been equally overwhelming across industries and has posed significant challenges in cloud migration and data preparation.
Considering the complexity, scale, and variety of data being generated today, cloud-based systems can offer businesses the desired flexibility, efficiency & scalability with their inherent high data storage and processing capacity. But a lot can happen while migrating on-premise data to the cloud or adopting hybrid migration solutions. From in-house technology skill gaps, security threats, connectivity issues, and cost challenges to unanticipated pitfalls in data migration mapping & timelines, several challenges can slow down the data migration initiative and hamper overall value in the end.
Take, for example, in-house technology skills gaps to efficiently manage the cloud. Migrating data to cloud-based big data environments requires extensive cloud & data expertise on the developers' part as well as the awareness of potential data integration challenges. In the absence of adequate cloud skills, organizations may struggle with cloud security concepts, data integration practices, data virtualization tools, modern-day data architecture, etc. Apart from technology skill gaps, there are connectivity challenges to consider during migration. The connectivity issues between two data sources can severely impact the process, leading to hampered productivity and even downtime. Ensuring a smooth flow of data from physical to virtual environments with well-thought-out plans for such contingencies would be vital for success.
Security and governance during migration can be another chief challenge. Businesses may often find maintaining data security during migration challenging as they are habituated to working within the realms of an on-premise data storage environment. The lack of close familiarity with modern cloud security practices can lead to ambiguity about ideal security and governance measures, such as implementing role-based access control for sensitive data, for example. Similarly, ensuring regulatory compliance can also become a critical challenge during the migration to avoid negative financial as well as reputational consequences.
Especially for businesses implementing cloud-based systems for the first time, there can be unanticipated challenges along with the possibility of potential gaps in the cloud migration strategy from the get-go. In the face of these and other challenges mentioned above, the role of a strategic cloud data migration partner may be pivotal to ensuring high cost-efficiency as well as effective and secure solution implementation. The right technology partner can also help you counter data preparation challenges along with helping to bypass the coding-intensive application integration process with strategic automation to lower operational costs and accelerate the desired outcomes.
Despite having volumes of enterprise data, most businesses struggle to effectively mobilize their data to derive actionable, real-time insights through modern analytics that can enhance organization-wide decision-making. The reason for this lies in unprepared data that can eat up significant resources in cleaning or transformation.
As data grows in complexity and variety in recent years, it is typically found in different data types in diverse systems driven by equally diverse functions. Since it is not ready-made for discovery and analysis, curating and prepping these diverse data sets becomes a huge demand for businesses to run successful analytics. Unprepared data also limits businesses from successfully leveraging analytics, blocking the opportunities for on-demand scalability.
Enabling data preparation environments that augment data analysts' and business users' ability to cleanse and prepare data without code-heavy approaches can significantly optimize the value of the entire initiative.
Sometimes, a mismatch between IT and business-level interpretations of data objectives or unawareness of industry best data analytics practices can negatively impact data initiatives and their success in the long run. Uniting diverse stakeholders with a single context and a similar degree of awareness of the importance of reliable and high-quality data for analytics may seem like an impossible challenge. Nonetheless, it is a vital imperative for organizations aiming to become data-driven businesses for higher agility and growth.
Dealing with these and more such data challenges brings forth the need to assess a business's existing ETL processes and their effectiveness in transforming volumes of structured, semi-structured, and non-structured data across on-premise, hybrid, or cloud environments. A holistic cloud data migration strategy would account for underlying assumptions and limitations of the current data environment and enhance it to meet the evolving business requirements in the age of data explosion.
Essentially, ETL and ELT employ the same three steps (Extract, Transform, Load) in a data transformation or data integration process. The difference between them, however, is the order in which these steps are implemented. That difference in order becomes a game-changer for some businesses depending on their unique data and analytics needs.
For nearly two decades, businesses have used Extract, Transform and Load (ETL) processing systems in their data warehousing and data integration functions. These traditional ETL systems are known to focus on data integration and data synchronization as per a structured organizational standard. This makes these systems a tried and tested data source. However, because these ETL systems depend upon a single source of data, and this data becomes a source for other business tools like the ones used to generate reports, databases, analytics, etc., the conclusions are drawn from these insights can have their limitations. Furthermore, the raw data undergoes processing and transformation before being loaded, decreasing the scope of maintaining data integrity. Also, from the context of expensive maintenance to long processing times to evolving data preparation needs, traditional ETL systems may not always be ideal for all businesses. This is especially true for businesses dealing with vast volumes of unstructured data with high complexity.
On the other hand, ELT systems can be highly scalable and are designed to curate data from various sources, including data lakes, flat files, remote repositories, etc. In an ELT system, the raw data is copied from one or more source system/s to a data destination, such as a data warehouse or other target store like a data lake. When your data transformations are complex requiring frequent changes at the end, ELT systems can offer the flexibility to perform these transformations after the data is loaded. Additionally, the availability of operational systems with a reduced dependency on mainframes, innovation in open-source database products, and a spike in coding talent that can easily handle modern-day ELT systems has also contributed to organizations swinging in favor of an ETL transformation.
However, with the frequently evolving data landscape, the needs of businesses too have evolved to a level where simply replacing traditional ETL processes with a newer ELT (Extract, Load, Transform) process may not be enough. What's needed is a highly strategic approach that not only considers a business's unique data analytics objectives, its swell-defined use cases for transformation, data architecture needs, operating environment, and the limitations of its existing enterprise systems but also envisions a customized solution that can create the most value for the business.
Today, businesses have to deal with a much more diverse data landscape than ever before, with new analytical functionalities and platforms emerging and becoming mainstream every day. With cloud to power the enterprises' growth visions, data capabilities must scale on-demand with a range of analytics use-cases, comprehensive data governance, and considerations for a variety of data sources with overwhelming complexity.
Traditional ETL systems that were predominantly designed to deal with generally well-structured data sets across monolithic applications can prove less effective in the age of cloud computing with ever-changing and ever-growing data sets and databases with decoupled components. These systems can also make it difficult to process data at lower costs and at higher speeds.
In the current data environments, it's highly likely that the common data destinations are no more data warehouses but data lakes for much more flexibility, storage, and scalability for end analytics. This is where utilizing the full benefits of a data lake can be challenging with traditional ETLs in the mix. Another key aspect that drives the need for ETL modernization is the limited self-service capabilities for the emerging data user profiles across an organization, owing to phenomenal changes in how data is being discovered, mined, stored, and analyzed in recent years. With the evolving data architecture, cloud migration plans, and increasing resource needs, ETL modernization can become a key piece of the puzzle.
There can also be scenarios where ETL modernization may not be enough. For example, implementing a data lake, cloud data warehouse, or AI-enabled data preparation for complex and frequent data transformations may require moving to ELT or even going beyond ELT to replace the 'Transform' component with data preparation platforms.
However, it is crucial to keep in mind that when it comes to moving from ETL to ELT, there is no one-size-fits-all solution. In any case, the shift from ETL to ELT or ETLT merits a comprehensive assessment as a key strategy for businesses to reinvent how data can be transformed to engineer increasingly relevant, fast, and reliable decisions.
Businesses working with huge quantities of both structured and unstructured data would find that they are able to process that data rather quickly if they would opt for ELT. That being said, if a business is handling smaller amounts of data and has found that ETL works for them, they can continue with the latter and need not necessarily make the switch.
Very often, organizations might opt for hybrid data storage solutions. Structured data might be stored in an on-premise environment, in remote repositories, or even on the cloud. In the case of unstructured, semi-structured data, etc., traditional ETL systems might not be able to handle such complex data from various sources. ELT systems, on the other hand, are better equipped to deal with such data.
Because most businesses operate on a severe time crunch, an ELT system is ideal for businesses that need all their data in one place very quickly. This is possible with ELT systems because they are designed to make speedy data transfers on priority.
Despite their limitations in the contemporary data and BI landscape, ETL systems have served a purpose that may still be relevant today for some businesses. For example, for businesses dealing with huge volumes of transactional data with security, privacy, and compliance concerns, forsaking the robustness of ETLs in favor of ELTs may not be the right choice.
Unlike pre-cloud environments, in most cases today, data may not be simply moving from point A to point B, but it can take different routes before finally landing at its destination. Maintaining data integrity in this journey may prove to be difficult with much less control and much less visibility of transformation logic within traditional ETLs. This is where cloud native ETLs can be the way to unlock immense value in data transformation.
A paradigm shift from running full data analyses in on-premise systems to running those in cloud systems in recent years has warranted the prevalence of cloud-native ETL solutions with their inherent flexibility, reliability, and scalability.
Running data transformation within the new cloud or hybrid environments can be much more efficient because cloud-native ETL solutions integrate well with on-premise systems, bringing in a significant cost advantage. These solutions can solve most critical integration and performance challenges in the face of different connectors, file formats, and other factors that can make cross-platform integration difficult. Additionally, higher elasticity, fault tolerance, and security offered by these solutions can optimize value even further.
Data ingestion challenges have been rising with businesses handling diverse datasets today, leading to increased complexity, security risks, and costs during the process. Cloud-native ETLs can make data ingestion much easier and more efficient for varying types and scales of transformations.
Cloud-native ETLs bring in another huge advantage in terms of freeing data teams from custodial and repetitive data tasks with low-code/no-code processes, parallel workflows, repeatable data pipelines, higher collaboration, and many more benefits for optimum efficiency.
There are many proven cloud-native ETL tools and platforms on the market, such as Microsoft Azure (Azure Data Factory i.e. ADF), AWS Glue, GCP Data Fusion, to name some of the top few. These tools are giving way to novel capabilities for businesses to optimize their data for enabling the teams for insights-driven decision making.
However, choosing the right tool for data transformation and implementing it around a business's unique needs and data environment is a decision that warrants scrutiny of diverse factors. With careful considerations for costs, security, vendor flexibility, and most importantly, your teams, cloud data migration with efficient data transformation doesn't have to be complicated or time-consuming. Furthermore, leveraging the strategic combinations of emerging concepts like data lakehouse, cloud-native tools, and new open-source frameworks like delta lake or reimagining automation possibilities for higher efficiency and scalability can create a significant value advantage for businesses when supported with the right expertise, a comprehensive cloud data migration strategy and implementation roadmap, and deep insight into the data ecosystem.
Hemant Belwalkar, VP – of Technology & Head of Data Engineering Practice at Bitwise, has over 20 years of extensive experience in Data Engineering, Data Modernization, ETL/ELT, Data Warehousing, Business Intelligence, Advanced Data Analytics, Big Data, and Cloud. Hemant has expertise in using data as a strategic asset, embracing new-age digital technologies such as AI/ML. His expertise in data engineering & architecture is far‐reaching, including enterprise solution design & implementation for Bitwise clients spanning across various industries and sizes.
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