What are the best ways to adopt new Data Warehousing technologies?

Far too often I've seen businesses trying to solve people and/or process problems in their data warehouses with new technology. Are the problems really due to technical limitations of the current technology stack, or are the problems rooted more in sketchy requirements, poor coding practices, and rush-to-market hastiness?

Before diving into new data warehousing technologies, I've found that assessing your current setup is vital. Here's a quick guide: Data Inventory: Catalog all data sources. Performance Check: Evaluate current warehouse performance. Data Governance: Review policies for compliance. Skills: Assess team expertise. Goals Alignment: Ensure alignment with business objectives. Cost Analysis: Understand the total cost of ownership.

From my experience, a clear understanding of the current data landscape is crucial. I've seen organizations jump into new technologies without a proper assessment, leading to costly mistakes. It's like trying to fit a square peg into a round hole. Always start with a thorough audit of your current data infrastructure and processes.

Unlike old days, we have enough tools and techniques to solve DW problems, complemented by low cost hardwares to support the ever-changing data ecosystem. However, I have seen both success and failures at scale. Despite having best in class ecosystem, it is crucial to have a clear understanding of the goal. Converting data to a meaningful information is as important as choosing the right intent. It is the situation that should guide the principal of DW. For example, I have built DW with user as epicentre as well as with product as epicentre. Both can use same data but will give different meaning. Data persuasion is easy if we can relate the information correctly. Choice of tools and technique becomes an obvious outcome of the situation.

In the perspective of a data analyst, "Assess Current Situation" involves evaluating existing data sources for quality, volume, integration, and access. This assessment helps identify data issues, user requirements, and regulatory considerations. The analyst examines the current data architecture, documentation, security measures, and user feedback to inform the adoption of new data warehousing technologies. Understanding the strengths and weaknesses of the current data environment is vital for shaping the strategy and requirements for the upcoming data warehousing solution.

Ensure that people who understand data warehousing approaches and corresponding technologies makes the decision. There are way too many self-proclaimed "enablers" who think they know everything from just Gartner reports or flashy marketing materials.

Selecting the right technology isn't just about the current buzz in the industry. I've been in situations where the "latest and greatest" wasn't necessarily the best fit. It's vital to consider the organization's long-term goals, the skill set of the team, and the nature of the data. Sometimes, a simpler, well-established solution might serve you better than a new entrant with all the bells and whistles. Always prioritize functionality and compatibility over trends.

There are multiple factors that govern the technology of choice. Given a choice, I would rather focus on three things - 1. Problem Statement, 2. Available Information, 3. Return on Investment. Based on these factors, we can choose the right stack. Eg. A tech centric company can choose Open Source Tech instead of COTS as the cost of dev and maintenance becomes easy whereas a service based organization can focus on business and let COTS take care of the rest. Contrary, there are many more factors influencing the decision hence a definite solution may not fit all.

Selecting the right data warehousing technology is crucial. Here's my approach: Assessment: Understand your business needs and goals. Options: Explore technologies like Snowflake, traditional databases, or hybrids. Compatibility: Ensure it integrates with existing systems. Scalability: Choose one that grows with your data. Performance: Look for speed-enhancing features. Cost Analysis: Analyze total ownership costs. Security: Ensure it meets compliance standards. Support: Check for vendor support and user community. Proof of Concept: Test it in a real-world scenario. Reputation: Research the vendor's track record. Choosing wisely ensures a successful transition to new data warehousing technologies.

The only real reason I can think of using an on-premises database server is to shorten the distance between the source data and the data warehouse. There are many different cloud warehouse and data analytics solutions (like those mentioned here) that bring true supercomputing power to these workloads, and at this point I'd assume that cloud would be the default for most people.

Migration isn't just about moving data; it's about ensuring data integrity throughout the process. I've found that a phased approach, where you migrate data in chunks and validate each chunk, can be effective. Also, always have a rollback plan. Things can go wrong, and you need a safety net.

Data warehouse is about 90% business analytics and data engineering, and about 10% ETL and database coding. Building good facts and dimensions demands a solid understanding of both the source systems *and* how the business wants to use the data warehouse. Kimball's bus matrix is an excellent tool to help keep these conversations in check.

Migration doesn't limit to ensuring seamless integration of data from various sources into the data warehouse. It also involves transforming and cleaning data to maintain accuracy and consistency. It's essential to use efficient ETL (Extract, Transform, Load) processes and tools, as well as data pipelines, to make data readily accessible for analysis. In view of a data analyst, proper data integration allows to work with high-quality, consolidated data, facilitating their ability to extract meaningful insights and make data-driven decisions.

Migrating to new data warehousing tech involves key steps: Assessment: Understand the current data setup and the new tech. Data Mapping: Plan what data to move, transform, or archive. Architecture: Design a scalable system that aligns with goals. Testing: Ensure data integrity and performance in the new environment. Data Cleansing: Clean and prepare data before migration. Migration Tools: Choose the right tools for data transfer. Parallel Run: Run old and new systems in parallel for consistency. User Training: Train the team on the new tech. Data Cutover: Plan a smooth transition time. Monitoring: Keep an eye on performance and optimize. A well-executed plan ensures a successful data warehousing migration.

- Identify unknowns and Perform POC/Work arounds before migration - Look for automation where ever possible for data movement, Validation and alert during discrepancies. - Some application are time stringent needs to be online 24/7 beware of the criticality so nice to have a well planned process before hitting the migration (possibly check for Offline migration). - Post migration have a plan to check whether the desired goal is achieved/achievable before/after production roll out. - Don't just migrate from old to new system there might be better options available in new system recommend and possibly implement as part of migration (rather than doing at later stage)

The success of a newly implemented system is intrinsically linked to the competence and acceptance of its users. Even the most advanced technological solutions can falter if end-users do not possess a comprehensive understanding or trust in the system's capabilities. To mitigate this risk, organizations should prioritize a multifaceted approach encompassing regular training sessions, interactive workshops, and hands-on demonstrations. These initiatives serve as pivotal mechanisms for closing the comprehension and trust gap between users and the technology they are expected to embrace.

A new system is only as good as its users. I've seen brilliant tech implementations fail because end-users didn't understand or trust the new system. Regular training sessions, workshops, and hands-on demos can bridge this gap. Remember, it's a change management process as much as a technical one.

When adopting new data warehousing tech, training and empowering users are key: Assessment: Understand user roles and needs for tailored training. Customized Training: Offer practical, hands-on sessions. Documentation: Provide clear reference materials. Continuous Learning: Encourage a culture of ongoing learning. User Support: Establish a robust support system. Feedback: Gather user feedback for improvements. Data Responsibility: Educate users on data ownership and governance. Empowerment: Enable users to leverage data for informed decisions. Feedback Loop: Maintain a channel for user suggestions. Empowered users are the driving force behind successful data warehousing tech adoption.

Every business has its "data treasures." These are the people that have probably worked there for a long time, and they have an intimate understanding of the data. They're often Excel wizards. These are the people that need to be sought out, and they need to be the first ones to look at new warehouse tables.

Any data and analytics solution, regardless of its potential, risks becoming costly shelf-ware if user experience isn't a top priority. To ensure the adoption, your solution must revolve around three core principles: accessibility, actionability, and transparency. First, your solution should empower users to act on the insights derived from data, through techniques like embedded analytics or the deployment of bots and low-code apps. Equally crucial is the transparency of your data. While it can be a laborious process, it's essential for creating a superior data environment. Transparency ensures that users understand the data sources, quality, and context, fostering trust and informed decision-making within the organization.

The first business intelligence solution built using a new data warehouse should be some sort of monitoring tool to monitor daily performance of the warehouse. This includes ETL run times, validation processes to ensure data integrity, and usage of the fact and dimension tables in the warehouse.

Post-migration, continuous monitoring is key. In the past, I've set up dashboards to keep an eye on performance metrics. Also, it's not a "set it and forget it" scenario. As data grows and business needs change, regular optimization is crucial to keep the warehouse performing at its peak.

When adopting new data warehousing tech, monitoring and optimizing performance are key: Continuous Monitoring: Regularly check key metrics to detect issues early. Query Optimization: Fine-tune queries for speed and efficiency. Resource Scaling: Ensure flexibility to prevent bottlenecks. Automated Alerts: Set up alerts for proactive issue resolution. Regular Reviews: Collaboratively identify optimization opportunities. Load Balancing: Distribute workloads evenly for resource efficiency. Index Maintenance: Schedule regular maintenance for efficient querying. Capacity Planning: Analyze growth trends for resource allocation. Monitoring and optimizing ensure peak performance of data warehousing technologies.

Always stay updated with the latest in the data warehousing world. Technologies and best practices evolve rapidly. Join forums, attend conferences, and be part of the community. It's not just about implementing a solution; it's about growing with it.

With respect to adopting standard techniques to be implemented for a Datawarehousing framework, we can consider the following checkpoints before: 1. Dividing datasets into chunks of data which needs to flow from OLTP databases 2. Deciding purpose of data required to be ingested into datawarehousing frameworks and frequency of data push. 3. Use of triggers from OLTP databases and setting up Batch jobs through a scheduling service. 4. Ensuring all required data is migrated to OLAP databases where we have defined the dimensions for analysing the data and scheduled jobs run without any issues. 4. After data extraction, working on transformation, loading, validation, testing, and monitoring data flows.