Podcast Summary
From Data Warehousing to Data Lakes and Back: The data industry has evolved from data warehousing to data lakes and back, with businesses moving data back to traditional warehouses in the cloud for better management and insights.
The data industry has seen significant evolution over the decades, from data warehousing to data lakes, and the current landscape involves a complex system of tools and technologies. In the 1980s, data warehousing emerged as a solution to help businesses gain insights from their data, leading to a market worth over $20 billion. However, as data types diversified and the need for machine learning and AI grew, challenges arose. Data lakes were introduced around a decade ago as a cost-effective storage solution, but managing and making sense of data in these lakes proved difficult. Today, businesses are moving subsets of their data back into traditional data warehouses in the cloud to improve data management and gain better insights. The modern data stack is a complex ecosystem, and it's essential to understand the history and evolution of these tools and technologies to effectively extract value from data.
Merging BI and MLAI through the lake house design pattern: The lake house design pattern enables direct BI, reporting, data science, and machine learning on data lakes, improving data analysis and decision-making efficiency
We are witnessing the convergence of Business Intelligence (BI) and Machine Learning and Artificial Intelligence (MLAI) through a new design pattern called the "lake house." This design pattern allows for direct BI, reporting, data science, and machine learning on data lakes. The similarities between these fields include the need for the same data, with machine learning requiring additional metadata for optimal results. However, the differences lie in the personas and organizational placement. Traditional BI and analytics are typically used by data analysts and business analysts, while machine learning is used by data scientists, machine learning engineers, and machine learning scientists. Some argue that simple regressions can be achieved using traditional data warehouses with SQL. However, a research project at UC Berkeley attempted to augment an existing relational model with machine learning, but the results were not satisfactory. Overall, the lake house design pattern represents the emerging trend of merging BI and MLAI markets, allowing for more efficient and effective data analysis and decision-making.
Integrating ML and Data Science with BI systems: Effectively integrate ML and data science with BI systems to minimize redundancy and maintain data consistency, rather than maintaining two separate copies of data.
Integrating machine learning and data science with traditional business intelligence (BI) systems can be challenging due to the technical differences between the two. Machine learning algorithms are iterative and recursive, making it difficult to implement them on top of data warehousing systems. However, the emergence of data frames as a lingua franca for data scientists has made it possible to marry the worlds of data science and machine learning with SQL and BI. Despite this progress, many enterprises still maintain two separate copies of their data – one in a data lake for machine learning and data science, and another in a data warehouse for SQL and BI. This architectural redundancy comes with a hefty price tag. The question is, do we really need two copies of the data and the associated maintenance costs, or can we do it all in one place? While the market for AI and machine learning is large and valuable, it's important to remember that there is also a significant existing workflow around BI. The answer to this challenge lies in finding a way to effectively integrate machine learning and data science with traditional BI systems while minimizing redundancy and maintaining data consistency.
Transforming data lakes into relational storage systems: Data lakes can now support OLAP queries and provide fast analytical queries through APIs by turning into structured relational storage systems, handling various data processing needs within the same system.
Data lakes can now support various data processing needs, including OLAP queries, by turning them into structured relational storage systems. This transformation is achieved by building transactionality into data lakes and adding schemas, quality metrics, and a SQL layer on top. By doing so, data can be reasoned about as structured data in tables, enabling fast analytical queries through APIs like data frame and SQL. This development caters to the market trend of processing data at different time speeds, addressing both batch and streaming analytics use cases, and providing various latency requirements. This approach offers the same performance as the fastest MPP (Massively Parallel Processing) engines while dealing with structured data. The importance of this development lies in its ability to handle various data processing needs within the same data lake, providing flexibility and efficiency for businesses.
Streaming systems simplify data processing: Streaming systems can reduce latency, eliminate manual reconciliation, and potentially make batch processing obsolete, making them a valuable addition to a modern data stack
The use of streaming systems in data processing can significantly reduce latency and simplify data operations, despite the common obsession with sub-5 millisecond latency for most use cases. The weakest link in the system, often an upstream process, can dictate latency, making it essential to ensure that data is loaded as quickly as possible. Streaming systems can handle all data operations, eliminating the need for manual reconciliation, joining tables, and dealing with late or inconsistent data. This can simplify data processing and potentially make batch data processing obsolete. A modern data stack, which is still evolving, likely includes a combination of streaming and batch processing systems to address various use cases. If given the freedom to build a data infrastructure from scratch in a large company, focusing on cloud-based solutions for both analytics and AIML would be a wise choice to avoid political battles and the complexity of on-premises solutions.
Embrace cloud's invisible networks for data processing: In cloud, process data directly into a data lake without schema, build a transactional layer, and use interactive data science environments for insights
When transitioning to a cloud native architecture, it's essential not to replicate the on-premises model. Instead, embrace the invisible networks in the cloud that allow for high-speed communication between machines and storage systems. This changes the game for data processing, as you can directly send data into a data lake without deciding on a schema upfront. However, to make sense of the data, you need to build a structural transactional layer on top of it. Additionally, an interactive data science environment is necessary for gaining insights from the data. This environment typically includes notebook solutions with technologies like Spark and can lead to operational machine learning platforms for training, tracking, and moving machine learning models into production.
Bridging the gap between data scientists and IT in machine learning: Effective machine learning requires a platform that connects data scientists and IT, with data pipelines and DAG tools transforming raw data and Delta Lake/Iceberg enabling BI tool integration.
For organizations looking to maximize the value of machine learning, it's crucial to have a machine learning platform that effectively bridges the gap between data scientists and IT. The data pipeline and DAG tools play a significant role in this process by transforming raw data into a format suitable for machine learning. However, the challenge lies in connecting traditional Business Intelligence (BI) tools to the data lake. With the emergence of technologies like Delta Lake and Iceberg, it's now possible to connect BI tools directly to the transactional layer of the data lake, making the migration to a data lake more accessible for companies without significant legacy issues. Success stories, such as Uber, demonstrate the competitive advantage of effective machine learning predictions, making the investment in a comprehensive machine learning platform worthwhile. Despite the challenges, the future of machine learning lies in seamless collaboration between data scientists, IT, and advanced data management solutions.
Leveraging modern technologies for effective pricing in ride-sharing services: To remain competitive, enterprises should ensure their data stack is multi-cloud, based on open standards, and uses machine learning and data science to extract valuable insights from raw data in a data lake.
Companies utilizing modern technologies and approaches, such as those discussed in the conversation around ride-sharing services, are leveraging machine learning and multi-cloud solutions to effectively meet the demands of surge pricing and provide accurate pricing to consumers. These companies, which are relatively new and have not been burdened by legacy systems, have built their stacks specifically for this use case, creating a significant competitive advantage. For enterprises looking to build their data strategy, it's essential to ensure their stack is multi-cloud and based on open standards and open-source technology. This approach provides the flexibility to adapt to changing technologies and avoid being locked into a specific stack. Additionally, storing data in raw format in a data lake is crucial as the amount of data being collected continues to grow. Machine learning and data science should also be prioritized as first-class citizens within the stack to extract valuable business insights from the data. While the exact shape of machine learning platforms may change, their core ingredients, such as machine learning and data science, will likely remain. By focusing on these areas, enterprises can effectively turn their data into valuable business insights.
