Migrating from a legacy reporting system to Looker has numerous advantages, with moving to the cloud, empowering your users with more agile access to their data, as well as granting a greater flexibility in how you deliver insights to your team. Looker is a true data platform, you can also use it to put the data where your users need it. Whether it is extending the platform with web actions, embedding visualizations in other tools, or providing a rich jumping off place for your users, Looker can turn your analysis into meaningful actions to enhance your business. Staying on your current system can reduce your team’s speed to insight, their time lost of switching between systems, and the friction of moving data between systems.
Analyzing your current system and your future needs is critical before starting the migration. Some factors to consider are:
Your Current Reporting Landscape - How much is being utilized? Do we need to migrate everything?
The Semantic Layer - Is your semantic layer answering all of your users’ questions? How can we add value - by adding data or broadening access?
User Workflows - How are your users using your system? Are they exporting to Excel to do their own analysis or are they using other tools in their workflow?
Now that we have determined the scope, the use cases, and the new capabilities in Looker we want to exploit, we need to start the actual migration.
Code Migration - We offer a Legacy to Looker Migration Service that accelerates the migration by programmatically converting your Legacy Semantic Layers to LookML. This conversion includes retaining formatting, data types, SQL definitions, and your custom views. By extracting the semantic layer programmatically, you can focus on meeting your user needs and not re-typing code.
Semantic Layer vs Explores - Traditionally, Legacy Semantic Layers are monolithic in nature. With Looker, the larger an explore, the harder it is for an analyst to find the fields that they need. After identifying your users’ needs, you need to break your semantic layers into digestible models and explores for each of your user groups. A measured approach to this migration can truly enhance your users’ experience, by speeding up the process of accessing their insights.
Report Migrations – Legacy systems often have a different report for every question, even if it’s just a top line answer. With Looker, you can build your semantic layer and analytics, so your users can answer questions quickly and don’t have to look over a wide table to find the answers. Here is where our analysis comes into play. By understanding what your users are asking, you don’t have to migrate every report, thus reducing costs and maintenance.
Case Study: Migration from Oracle OBIEE to Looker
Reduce Technical Debt- When migrating your underlying semantic layer to LookML, it also make sense to reduce your technical debt by simplifying the code when possible using extends in the explores. This is to ensure that you are not repeating code and migrating any reports that are superfluous or haven't been used. Use this opportunity to ensure that your Looker deployment is lean and mean.
If part of your transformation also includes switching databases, we can help you convert that code to your new database with our Evove offering. Find out more here .
Workflow Enhancements - While you are making a shift in solutioning, it makes sense to examine your workflows as well. Looker's capabilities allow you to integrate embedded analytics in other tools, use web actions to start a new workflow from your insights, or even create alerts when something needs attention. This simplification turns your reporting into action-inducing analysis, increasing your users’ effectiveness.
Extends, Derived Tables, and Performance Tuning - You also want to make sure that your system hums when you roll it out to your users. Maximize your Looker deployment by using persisting derived tables (PDTs), extends in explores, and caching often used data to ensure that your users can make their insights fast and hassle-free.
The cost of migrating from one system to another has often been a reason to maintain the status quo. At Trianz, we help you reduce that cost by shortening your time to production with our Legacy to Looker Migration service, reduce your technical debt, and maximize the capabilities of your Looker Instance.
Ready to start your migration? Reach out to us at [email protected]
Director of Analytics Solutioning at Trianz
With over a decade of experience in the analytics space, Andrew has had great success helping clients migrate to the newest technologies and making the best of them.
Contact Us Today
Connecting more people to data has become imperative for organizations worldwide. In Top Trends in Data & Analytics for 2022, Gartner stated, “Connections between diverse and distributed data and people create truly impactful insight and innovation. These connections are critical to assisting humans and machines in making quicker, more accurate, trustworthy, and contextualized decisions while considering an increasing number of factors, stakeholders, and data sources.”Explore
Since the dawn of business, users have looked for three main components when it comes to data: Search | Secure| Share. Now let's talk about the evolution of data over the years. It's a story in itself if one pays attention. Back then, applications were created to handle a set of processes/tasks. These processes/tasks, when grouped logically, became a sub-function, a set of sub-functions constituted a function, and a set of functions made up an enterprise. Phase 1 – Data-AwareExplore
Practitioners in the data realm have gone through various acronyms over the years. It all started with "Decision Support Systems" followed by "Data Warehouse", "Data Marts", "Data Lakes", "Data Fabric", and "Data Mesh", amongst storage formats of RDBMS, MPP, Big Data, Blob, Parquet, Iceberg, etc., and data collection, consolidation, and consumption patterns that have evolved with technology.Explore
Enterprises have, over time, invested in a variety of tools, technologies, and methodologies to solve the critical problem of managing enterprise data assets, be it data catalogs, security policies associated with data access, or encryption/decryption of data (in motion and at rest) or identification of PII, PHI, PCI data. As technology has evolved, so have the tools and methodologies to implement the same. However, the issue continues to persist. There are a variety of reasons for the same:Explore
Finding Hidden Patterns and Correlations Innovative technologies such as artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) are transforming the way we approach data analytics. AI, ML and NLP are categorized under the umbrella term of “cognitive analytics,” which is an approach that leverages human-like computer intelligence to identify hidden patterns and correlations in data.Explore
The Rise in Big Data Analytics According to Internet World Stats, global internet usage increased by 1,339.6% between 2000-2021. With nearly thirteen times as many people using the internet, this has resulted in a massive increase in the amount of data being processed daily. Our increased sharing and consumption of digital media also compounds this increased usage to create an enormous pool of data for big data analytics firms to process.Explore