Podcast Summary
Data Lakes vs Data Warehouses: Unique Use Cases: Data Lakes are optimized for unstructured data and operational AI use cases, while Data Warehouses are optimized for analytics workflows and query patterns. Both can do what the other one does, but industry trends suggest SQL data warehouses may replace data lakes for structured and semi-structured data in the future.
Data lakes and data warehouses serve distinct purposes and are optimized for different use cases. Martine Casado, a 16z general partner and pioneer of software defined networking, argued that data lakes, which store tabular data in open source file formats like parquet or orc in public cloud object storage, are better suited for unstructured data and operational AI use cases that are compute-intensive. On the other hand, data warehouses, which use object storage to store their data and provide some advantages of data lakes, are optimized for analytics workflows and query patterns. Although both technologies can technically do what the other one does, the industry is making decisions based on the primary use cases they are being used for. Five years from now, Bob Muglia, the former CEO of Snowflake, believes that data will primarily sit behind a SQL prompt, and SQL data warehouses will replace data lakes for storing structured and semi-structured data. However, Martine sees the operational AI use cases growing faster and argues that over time, data lakes may end up consuming everything. This debate highlights the importance of understanding the unique strengths and limitations of different data architectures and choosing the right one based on specific use cases.
Handling complex data types in data warehouses: Data warehouses may not effectively manage complex data types like images, videos, and documents, but this functionality is expected to be added in the future. SQL relational data warehouses are predicted to dominate data retrieval, but processing complex data will likely require specialized tools and approaches.
While cloud SQL data warehouses are sufficient for handling structured and semi-structured data, they currently lack the capability to effectively manage complex data types such as images, videos, and documents. However, this functionality is expected to be added in the next 2-3 years. SQL relational data warehouses have historically dominated data retrieval, but the technology required for processing complex data is fundamentally different. Although SQL may eventually win in data processing as well, it is predicted to take 8-10 years for this to occur. The speaker argues that organizations will store complex data in a data lake only once, rather than maintaining separate copies in both the data lake and data warehouse. Overall, the evolution of data management systems will continue to favor relational databases for data retrieval, but the processing of complex data will likely require specialized tools and approaches.
The Future of Data Warehouses and Data Lakes: Both data warehouses and data lakes are evolving to support diverse access patterns, SQL, and procedural operations. The AI/ML domain is driving the growth of data lakes, emphasizing open formats and interoperability. The future may see a convergence of these technologies, with a focus on optimizing for specific use cases.
As data usage evolves, the distinction between data warehouses and data lakes is becoming less clear. Both systems will need to support various access patterns, SQL, and procedural operations to cater to diverse use cases. The future may see a convergence of these technologies, with companies like Snowflake and Databricks offering both declarative and procedural approaches. The data lake is gaining traction, particularly in the AI/ML domain, where complex models are being built and served. Use cases driving the technology include analytics, dashboarding, and creating complex models for applications like wait time prediction, fraud detection, and dynamic pricing. The growth in the AI/ML space suggests that this area will influence the technological architecture. Another key point is the importance of open formats and interoperability for various use cases. Open source file formats, indexing, and metadata are essential for both data warehouses and data lakes. The ability to input, output, and convert formats easily is crucial for handling the diverse operations required in the data processing landscape. In the coming years, we'll likely see a continued evolution of these systems, with a focus on optimizing for specific use cases. However, the use case itself will ultimately dictate the technology, as the industry moves towards a converged point where both declarative and procedural approaches can coexist.
Convergence of Data Science and Analytics: Data lakes will evolve, with a unifying layer for querying and serving data, while notebooks facilitate the combination of data, code, and visualizations. Machine learning and analytics communities are converging, with Google BigQuery leading the way by integrating machine learning into SQL.
The data science and analytics communities are continuing to evolve and converge, with specialized stacks becoming more prevalent due to resource constraints. The data lake will remain important for storing various data types, but its structure and usage will change as we move towards greater understanding of what data is truly valuable. The movement of data will decrease, and there will be a need for a unifying layer at the top to facilitate querying and serving information. Notebooks, with their language-agnostic approach and ability to combine data, code, and visualizations, are well-suited for this role. Another major topic discussed was the convergence of the machine learning and analytics worlds. Despite syncing the same data sources, these two communities have remained largely separate due to tooling inconveniences. There are three visions for bringing these worlds together: integrating machine learning into SQL, putting SQL into machine learning environments, or creating a new, unified platform. Google BigQuery is currently leading the charge in integrating machine learning into SQL. Ultimately, the goal is to make it easier for these communities to work together and access the same data, enabling more effective and efficient data analysis and machine learning applications.
Future of Data Processing and Machine Learning: Heterogeneous and Fragmented Systems: The future of data processing and machine learning involves a heterogeneous, fragmented system with multiple systems interacting through common formats like Arrow, while addressing technical challenges of interoperability and creating efficient workflows for data engineers and data scientists.
The future of data processing and machine learning is likely to involve a heterogeneous, fragmented system with multiple systems interacting through common formats. According to the discussion, there are different visions for this future, including SQL integrated with Python or Scala, Arrow as an interchange format, and specialization in deep learning versus predictive models. While Arrow is seen as a significant step forward for providing a consistent in-memory layout for advanced analytics, it doesn't completely solve the technical challenges of interoperability, such as egress fees and cloud servers being in different locations. The use cases and personas of data engineers and data scientists require different skills, and the current infrastructure separating data prep and feature engineering from machine learning model training creates a significant technical slowness. Despite this, having multiple languages and systems is not due to one being turning complete or the other not, but rather because people build their workflows around them. Therefore, open interfaces and common formats like Arrow are crucial for enabling efficient data processing and machine learning in a heterogeneous and fragmented system.
Integrating relational databases with predictive analytics using knowledge graphs: Hybrid systems combining relational and predictive capabilities will dominate, knowledge graphs will be essential for business modeling and predictive analytics in the 2030s, and data mesh is gaining traction as a solution to current data challenges
The future of data systems lies in the integration of relational databases with predictive analytics using knowledge graphs. Hybrid systems, combining both predictive and relational capabilities, will dominate for the next few years. However, as SQL reaches its limits in data modeling and transformation, knowledge graphs will become essential for business modeling and predictive analytics in the 2030s. Additionally, the concept of data mesh, which involves decentralizing data processing and analytics into individual business units, is gaining traction as a potential solution to current challenges, such as cost, data quality, and lack of business context. Data mesh aims to put technology where the data and knowledge reside, and it offers valuable ideas for organizing and managing data across large enterprises. Despite the potential of streaming data and in-flight transforms, I believe that data is not purely streaming, and a more comprehensive approach is needed for effective data management and analysis.
Maintaining data consistency in the modern data stack: The modern data stack prioritizes flexibility and handling various use cases, while ensuring data consistency through a unified architecture, rather than relying on traditional streaming-based solutions that neglect consistency for distribution.
Data consistency is crucial when managing data, and transactional data from business systems is an essential source that cannot be overlooked. Traditional streaming-based solutions often neglect this aspect, prioritizing distribution over consistency. However, building fully distributed architectures can lead to inefficiencies and separate administration, processing, and access to toolsets. The term "data mesh" can be misleading, as it connotes full distribution, but the goal should be to enable distributed nature with a unified architecture. The modern data stack, with its flexibility and capability to handle various use cases, is expected to continue absorbing new applications, such as complex data from predictive analytics and medical fields, in the coming years. Ultimately, the aim is to have one clean, well-understood data set that supports performance, large batch analytical processing, and data science, while accommodating specialized use cases.
The Future of Data Processing: Modern Data Apps and Latency Challenges: Modern data apps will revolutionize business decision-making with real-time data processing, but designers must consider latency and throughput trade-offs.
The future of data processing lies in the modern data app, which can autonomously make business decisions using data from various systems. However, building such data apps comes with challenges, particularly around latency. While some believe data apps should be separate systems that pull data from data warehouses, others argue for natively built data apps. Regarding latency, while some applications require instant response, most can work with a minute or two of delay. The trade-off between latency and throughput is a complex issue, and designers must consider it case by case. Despite these challenges, the future of data processing will involve more automation and real-time decision-making, making the development of modern data apps a crucial endeavor.
Data platforms trade-offs between latency and throughput: New data platforms may offer a combination of streaming and batch processing, allowing users to choose the best solution for their specific needs based on latency requirements.
As data platforms continue to evolve, there will be ongoing trade-offs between latency and throughput. While throughput-optimized architectures like Snowflake are expected to go lower in latency than anticipated, they may not be the best solution for applications requiring extremely low latency. The future may bring new major data platforms alongside the current players like Snowflake, Databricks, Google, AWS, and Azure. These new platforms could offer a combination of streaming and batch processing, allowing users to choose the best solution for their specific needs. However, it's important to remember that architectural choices will impact the latency characteristics of each platform differently. For instance, Snowflake's architecture differs from MIM SQL's in terms of latency. The conversation also touched on the potential for Lambda architectures without the need for additional tools, which could offer the benefits of both streaming and batch processing. In conclusion, the data platform landscape will continue to evolve, and users will need to make informed decisions based on their specific use cases and latency requirements.
