The landscape of analytics has undergone a dramatic transformation, driven by the increasing complexity of data and the ever-growing demand for timely insights. Legacy BI tools, once the industry standard, are now struggling to keep pace with the evolving needs of modern businesses. The need to modernize these legacy reports has become imperative to unlock the full potential of data-driven decision-making and Generative AI assisted analytics.

Let’s dive into what makes Looker a compelling BI tool and explore modernization challenges when migrating legacy BI from tools like Cognos and Tableau to Looker.

Why Looker for Modern BI

Looker, a powerful data platform offered by Google Cloud, has emerged as a leading choice to modernize BI, especially for companies already using Google BigQuery.

Key Features of Looker for Modern Analytics

Looker offers a unique blend of features that make it an ideal solution for businesses seeking to accelerate time-to-value from their data.

• Intuitive User Interface: Looker’s user-friendly interface empowers business users to explore data independently, reducing reliance on technical teams.
• Semantic Layer: This powerful abstraction layer provides a unified view of data across disparate sources, simplifying data exploration and analysis.
• Advanced Analytics: Looker enables advanced analytics techniques like machine learning and predictive modeling, allowing organizations to uncover deeper insights.
• Customization and Flexibility: Looker can be tailored to meet the specific needs of any organization, with customizable dashboards, reports, and alerts.
• Collaboration and Sharing: Seamless collaboration and sharing of insights across teams foster a data-driven culture.
• AI-Powered Analytics: Gemini in Looker provides AI assisted analytical workflows for more efficient self-service BI.

Why BigQuery Customers Modernize BI Reports in Looker

Specifically for Google BigQuery customers that are using other BI tools, there are many key drivers for modernizing those reports in Looker.

• LookML provides a re-usable data model that is highly efficient within the BigQuery / GCP ecosystem.
• Looker and BigQuery deliver faster query performance – up to 10x improved speeds.
• Looker enables easy integration with BigQuery data sources.
• Looker excels at complex data modeling, customization of reports, and real-time insights into data.
• Looker works well with large volumes of data from multiple sources.

Now that we’ve explored why Looker is worth considering for your modern BI needs, let’s take a look at migration challenges.

BI Migration Challenges

Migrating from legacy BI tools to Looker can be a complex process and can present challenges, including issues related to data migration, report and dashboard reconstruction, user adoption, and integration with existing systems.

Common Challenges When Migrating to Looker

Looker offers unique features that require proven solutions and workarounds to achieve similar or better results when migrating from legacy BI tools.

• Data Model Differences: Each BI platform has different data modelling approaches, leading to discrepancies in data representation and calculations.
• Excel Migration: Looker does not have direct Excel import, so data needs to be extracted and converted.
• User Training Needs: Users familiar with their existing BI tools may have difficulty adapting to Looker’s interface, navigation, and functionalities.
• Data Blending in Looker: Data Blending, a widely used method for combining multiple sources, is not a feature in Looker and requires alternative methods for achieving similar outcomes.
• Visualization Variances: Every BI tool offers its unique set of visualizations or formatting options, impacting the layout and design of dashboards.
• Performance Optimization: Large data sets and complex queries can have an impact on performance without proper query optimization.

Tableau Migration to Looker Success Story

With extensive BI Modernization experience, Bitwise understands the differences between legacy tools and modern tools like Looker. We’ve been through the migration process and have developed solutions to address the unique challenges of migrating to Looker with proven success in keeping BI migrations on track and within budget.

For instance, a leading American ticketing company faced the challenge of maintaining and scaling its existing Tableau reports. As their data volume and complexity grew, they sought a modern, scalable solution to streamline their BI operations and unlock deeper insights. Bitwise solution helped the firm in migrating Tableau Report Migration to Looker while reducing the development and maintenance costs.

Recommendations for BI Modernization

To overcome the challenges mentioned above, a structured approach to legacy BI modernization is essential.

Proven BI Modernization Approach

Bitwise recommends the following approach to ensuring a seamless transition from legacy BI to Looker.

• Assessment: Evaluate the current BI landscape, identify pain points, and define modernization goals.
• Planning: Develop a detailed migration plan, including data migration strategies, report and dashboard mapping, and user training.
• Implementation: Migrate data, build reports and dashboards, and configure Looker to meet specific business needs.
• Testing and Validation: Thoroughly test the migrated environment to ensure data accuracy and report functionality.
• Deployment and User Training: Deploy Looker to end-users and provide comprehensive training to maximize adoption.
• Ongoing Support and Optimization: Continuously monitor and optimize the Looker environment to ensure optimal performance and user satisfaction.

Access our whitepaper on modernizing BI and transforming data into actionable insights to know more about a strategic approach to modernizing your legacy BI environments to become truly data driven.

Achieving BI Modernization Success with Looker

Using the above approach, Bitwise helped a global financial services company that was struggling with a legacy WebFocus-based reporting and visualization application. The company was plagued by high maintenance costs, performance issues, and poor user experience.

To address these challenges, they partnered with Bitwise to modernize legacy reporting on the cloud with Looker. By leveraging Looker’s powerful analytics capabilities, the company significantly improved report performance, reduced costs, and enhanced user experience. The intuitive interface and self-service capabilities empowered users to access real-time data and generate insights more efficiently, driving data-driven decision-making accelerating business growth.

Getting Started

While there are many challenges to modernizing BI in Looker, the benefits can outweigh the challenges and set your organization up for a modern, data-driven future. In addition to using a proven migration approach, Bitwise, a trusted Google Cloud Partner and leading provider of data solutions, offers a range of accelerators and value-adds to expedite the BI modernization process.

Our proven BI modernization methodology will guide you through the entire process, from assessment to deployment. Contact us today to start your BI Modernization journey and accelerate time-to-value with Looker.

The post Accelerating Time-to-Value with Looker: The Future of BI appeared first on Bitwise.

Challenges of migrating PL/SQL to cloud

There are a number of options for converting PL/SQL code to cloud-native systems. Tools like SnowConvert from Snowflake and AWS Schema Conversion tool can apply for certain scenarios and there are manual conversion and other third-party tool options.

Even with these tools, migrating PL/SQL code to Synapse or Snowflake can be a challenging and time-consuming process. Challenges include:

• Understanding the legacy code – PL/SQL code can be complex and difficult to understand, especially for code that was written many years ago.
• Reproducing the functionality – The goal of the migration is to reproduce the same functionality as the legacy code in the new environment. This can be difficult to do, especially if the code is not well-documented.
• Testing the migrated code – Once the code has been migrated, it needs to be thoroughly tested to ensure that it is working correctly. This can be a time-consuming and error-prone process.

Using AI to overcome challenges and accelerate data modernization

When harnessed properly, artificial intelligence (AI) can help overcome a lot of the complexity that causes challenges when migrating to the cloud. Key areas where you can use AI to modernize your PL/SQL code in Synapse or Snowflake include:

• Analyze the legacy code – AI can help identify patterns, dependencies, other important information that can be used to make the code easier to understand and accelerate migration.
• Generate new code – using AI to replicate the functionality of the legacy PL/SQL code can save a significant amount of time and effort when converting to Synapse or Snowflake.
• Test the migrated code – testing the migrated code and identifying any errors or defects is a critically important and difficult step in the modernization process, which can be assisted with AI to ensure that the migrated code is working correctly.

Generative AI approach to PL/SQL code conversion

Generative AI opens new doors for confronting the issues of tedious code conversion and optimization to accelerate your data modernization journey. With our advanced knowledge of PL/SQL code and deep experience modernizing data in Synapse and Snowflake, Bitwise has created powerful modules for transforming and validating code, including:

• Code Converter – provides effortless conversion that automates code migration and modernization utilizing Gen AI. Its batch processing feature allows increased efficiency for automated PL/SQL conversion.
• Code Optimizer – assesses code with Gen AI and suggests optimization designed to your specific goals. Code optimizer reduces space complexity, time and improves error handling assuring fine-tuned performance.
• Code Documenter – automates the commenting and documentation process allowing a clear, comprehensible code base and can delve into variables, functions and defined objects. This not only enhances code readability but also adding to long-term maintainability.
• Migrated Code Validation Utility – provides support for heterogeneous data sources and file types using a unique approach of in-memory comparison without moving the data across data stores. The utility generates comprehensive comparison and summary reports with synopsis of mismatched data and overall comparison stats to pinpoint any potential errors.

Conclusion

AI-powered PL/SQL code conversion to Synapse and Snowflake can be a challenging task, but it is a necessary step for enterprises that are looking to modernize their data and move to the cloud. AI can be used to overcome the challenges of migration and accelerate data modernization initiatives.

While using AI can be a game-changer for modernizing your PL/SQL code in Synapse or Snowflake, building the right AI competencies and using optimal prompt engineering with Generative AI comes with its own set of challenges. Our team has gone through extensive trial and error to perfect the steps needed to effectively harness AI to successfully convert PL/SQL code. Explore our Data Migration and Modernization solutions to see how we accelerate PL/SQL code conversion.

The post How to use AI to modernize your PL/SQL code in Synapse or Snowflake appeared first on Bitwise.