Blog

Data science and big data technology. Data scientist computing, analysing and visualizing complex data set on computer. Data mining, artificial intelligence, machine learning, business analytics.

&nbsp;What needs to change in the world of data management to support AI?&nbsp;Data quality has always been crucial to any data-driven initiative, but in the age of AI and ML, ensuring the quality of data has become more important than ever. AI systems are only as effective as the data they are trained on, and the quality of this data directly impacts the performance and reliability of these models. One of the most widely recognized frameworks for ensuring data quality is the CARE method, which stands for Completeness, Accuracy, Relevance, and Enough to Work With. This methodology, which has traditionally been applied in the context of business intelligence, has evolved to address the unique challenges posed by AI, helping organizations ensure that the data they feed into AI systems is of the highest quality.<h3>What is the CARE Method?</h3>The CARE framework focuses on four key components: Completeness: The data should have all the necessary attributes and records, with no missing or incomplete data entries.Accuracy: The data must accurately represent the real-world values it is meant to model, without errors or misinterpretations.Relevance: The data should be pertinent to the business or operational needs, ensuring it is aligned with the goals of the AI model.Enough to Work With: This concept, while often interpreted as a measure of volume or scale, essentially means having sufficient quality and quantity of data to allow AI systems to learn meaningful patterns. Without enough usable data, AI models may struggle to generalize or produce reliable results.While these principles have always been important, the advent of AI has added layers of complexity, making each element of the CARE framework even more vital. The demand for high-quality data in AI applications has evolved, and ensuring data meets the CARE standards is now a key step in delivering effective and trustworthy AI systems.<h3>The Evolving Role of the CARE Method in AI</h3>Completeness: In traditional data management, completeness meant ensuring datasets had no missing values or incomplete records. In the world of AI, completeness goes beyond merely filling in gaps—it’s about ensuring that AI training datasets are diverse and inclusive enough to help the model learn without introducing bias or inaccuracies. In AI applications, incomplete or biased data can lead to poor model performance or even catastrophic outcomes. For example, in healthcare AI, missing or incomplete patient data can result in inaccurate diagnoses or treatment recommendations. AI systems today emphasize:<ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>Data augmentation and synthetic data generation techniques to fill gaps in training sets. See my whole <a data-mce-href="https://data-sphere-chronicle.com/blog/two-sides-of-synthetic-data" href="https://data-sphere-chronicle.com/blog/two-sides-of-synthetic-data">analysis of synthetic data here</a>. </li><li>Data imputation techniques, using algorithms to predict missing values based on existing data.</li><li>Ensuring balanced datasets to avoid skewed predictions, especially for underrepresented categories.</li></ul>The importance of completeness in AI has shifted from filling in missing values to ensuring that the entire dataset captures the diversity needed for accurate model learning.Accuracy: Accuracy in traditional data management was about ensuring that data reflected reality. For AI, accuracy is even more critical—small inaccuracies can snowball into large errors when AI models are deployed in real-world applications. A slight mistake in training data can cause the model to generate incorrect predictions that have serious consequences, especially in sectors like finance or healthcare. To ensure accuracy in AI, companies have to:<ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>Implement real-time data validation to detect errors before data enters the system.</li><li>Employ cross-validation techniques during model training to check that the model is learning the right patterns from the data.</li><li>Use error detection tools that continuously monitor data and model performance after deployment.</li></ul>Accuracy ensures that AI doesn’t just produce predictions—it produces reliable, actionable insights. The demand for precision has never been higher.Relevance: For traditional data use cases, relevance meant ensuring that the data served the business objectives. For AI, relevance goes further. AI models often work with large volumes of data, and relevance refers to ensuring that only the most useful data is being processed by the model. Irrelevant or extraneous data can introduce noise, reducing model performance. For AI, relevance includes:<ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>Feature engineering, where only the most important variables (features) are selected for model training.</li><li>Data curation to ensure that the data reflects current trends, behaviors, or external factors that are critical for prediction.</li></ul>To avoid the pitfalls of irrelevant data, AI models demand that data be curated to ensure it aligns with the task or problem the model is solving. The relevance of data is directly tied to the model’s ability to produce high-quality outputs.Enough to Work With: Traditionally, the Enough to Work With component referred to ensuring that there was a sufficient amount of data to make decisions. For AI, this principle focuses on having enough high-quality, diverse data to train the model adequately. This means not just having large volumes of data, but ensuring that the data is representative of the problem the model will solve. For AI models to be effective, they need to learn from vast amounts of data, but that data must also be varied and comprehensive enough to reflect the nuances of real-world scenarios. Without enough data:<ul style="list-style-type: square;" data-mce-style="list-style-type: square;"><li>AI models risk overfitting, where they learn the noise in the data rather than the signal.</li><li>The model may fail to generalize well to new, unseen data, reducing its effectiveness in real-world applications.</li></ul>Ensuring there is enough diverse, high-quality data has become one of the most challenging aspects of AI. AI projects now focus on acquiring diverse datasets that can capture the full spectrum of conditions under which the model will operate.<h3>Why the CARE Method is More Important for AI Than Ever</h3>With the growing dependence on AI to make decisions, the importance of high-quality data cannot be overstated. AI systems rely heavily on the data they are trained on, and poor-quality data can lead to biased, inaccurate, or unethical outcomes. The CARE methodology provides a clear framework for addressing these challenges and ensuring data is suitable for AI.However, as AI has evolved, so too has the complexity of the data it requires. Ensuring completeness, accuracy, relevance, and enough to work with is not only important for model performance, but it’s also essential for the ethical and fair deployment of AI systems. As organizations continue to integrate AI into their operations, maintaining high data quality through the CARE method is key to mitigating risks and maximizing the benefits of AI. By revisiting and refining the CARE framework to address the growing needs of AI, businesses can ensure that their AI systems are not only powerful but also responsible and reliable. The future of AI depends on carefully curated, accurate, relevant, and comprehensive data—and the CARE method is more critical than ever in the future.

Evolving to Meet the Demands of AI

The world of data warehousing is evolving rapidly, with organizations handling ever-growing datasets and seeking tools that balance performance, flexibility, and cost. Let's talk about the different data warehousing technologies to support data lakes and data fabric operations. Among the many options available today, Apache Iceberg has been gaining attention for its innovative approach. Let's take a closer look at Iceberg’s unique features and contrasts it with popular alternatives like Amazon Redshift and Snowflake.<h3>Apache Iceberg: The Basics</h3>Apache Iceberg is an open table format designed to simplify and improve the way organizations manage large analytic datasets. Its appeal lies in its flexibility and the fact that it integrates with a range of existing tools, including Apache Spark, Kafka, Flink, and Hive. This means businesses already working with these technologies can incorporate Iceberg without overhauling their systems. Its design is also intuitive, often described as "pythonic," making it accessible for developers and data teams familiar with Python-based workflows.<h3>Why Iceberg Stands Out</h3>One of Iceberg’s standout features is its hidden partitioning. Unlike traditional approaches where users must manually define and manage partitions, Iceberg automates this process. This is a big win for teams, as it reduces the complexity of data management and minimizes human error. Another major strength is its cloud-agnostic nature. Iceberg can work with any object storage—whether it’s Amazon S3, Google Cloud Storage, Azure Blob Storage, or even on-premises systems like Hadoop Distributed File System (HDFS). Iceberg enables the use of object storage while decoupling compute, allowing organizations to scale resources independently and optimize costs. This flexibility offers organizations freedom from vendor lock-in and the ability to adopt multi-cloud strategies, which are increasingly common. Additionally, Iceberg supports schema evolution, allowing users to make changes to the data model—like adding or renaming columns—without breaking existing queries or rewriting historical data. Other features like time travel for querying historical snapshots and support for ACID transactions further enhance its appeal.<h3>How Iceberg Compares to Redshift and Snowflake</h3>Of course, Iceberg isn’t the only option for data warehousing. Managed services like Amazon Redshift and Snowflake remain go-to choices for many organizations, and for good reason. Amazon Redshift is a petabyte-scale, fully managed data warehouse designed for performance. It’s tightly integrated with the AWS ecosystem, making it a natural fit for businesses already using AWS services - like S3, Lambda, and Glue, providing a unified analytics experience. Redshift is optimized for SQL-based analytics, offering fast query performance using columnar storage and massively parallel processing (MPP).However, it requires explicit partitioning and lacks the flexibility of Iceberg’s schema evolution and open architecture. Additionally, its compute and storage aren’t fully decoupled, which can sometimes lead to cost inefficiencies.Snowflake, on the other hand, has made a name for itself with its simplicity and multi-cloud support. Unlike Redshift, Snowflake works across AWS, Azure, and Google Cloud, giving users more flexibility. It’s also designed to scale compute and storage independently, which helps optimize costs. Snowflake has advanced features like built-in support for semi-structured data (e.g., JSON), data sharing, and cloning that make Snowflake highly versatile. That said, Snowflake uses a proprietary data format, which can lead to concerns about vendor lock-in. While Snowflake excels as a managed platform, it lacks the open, integration-friendly nature, which is critical for organizations seeking flexibility and long-term control over their data.<h3>Opinion: Where Iceberg Fits</h3>Ultimately, all of these technologies are great - it just depends on your use case and budget. All offer enterprise data warehousing solutions that can scale to what organizations need now and will need in the future. In many ways, Iceberg feels like a response to the challenges of traditional data warehousing tools. Its open architecture, cloud-agnostic design, and seamless integration with widely used technologies make it particularly appealing for organizations looking to modernize their data infrastructure. That said, it’s not a managed solution like Snowflake or Redshift, so teams adopting Iceberg should be prepared to invest in managing and maintaining their setup.&nbsp; For businesses that value flexibility and control—particularly those already using Apache tools—Iceberg offers a compelling alternative. It’s not a one-size-fits-all solution, but it’s a thoughtful approach to data warehousing that’s well-suited to the demands of modern, cloud-native data strategies.

Iceberg and other Data Warehousing Technologies

Generative AI models, such as large language models (LLMs), have upturned various industries by automating complex tasks and generating human-like content. A critical aspect of their operation is inference, the process where a trained model processes input data to produce an output. Each interaction with an AI system involves inference, making it a fundamental component of AI deployment. While the benefits of AI are clear and well documented - there is no free lunch. Inference - or the cost per inference - is an essential part of estimating the value we can gain from AI. Let's explore how AI inference consumes computing resources, energy, and ultimately dollars. <h3>The Mechanics of Inference</h3>Inference involves feeding input data into a pre-trained model to obtain predictions or generate content. Unlike the training phase, which is computationally intensive and performed once, inference occurs every time the model is used, necessitating efficient execution to ensure responsiveness and scalability. Let's say you ask ChatGPT a question and you get a response. That is one inference - which is currently estimated to be a few cents for your average transaction. Now multiply that by all users at any given time - you can see how inference cost is the majority of an AI deployment's runtime costs. <h3>Factors Influencing Inference Costs</h3>Several factors impact the cost and efficiency of inference, and two of the most important ones are token size and context windows. These elements shape how the AI processes input data, which in turn impacts computational requirements, latency, and overall costs. <h3>Token Size </h3>A token is a fundamental unit of data that a model processes. In language models, tokens often represent words, subwords, or even individual characters. For example: The phrase “Artificial Intelligence is transformative” might be broken into tokens as ["Artificial", "Intelligence", "is", "transformative"].Depending on the tokenizer, it could also be segmented as ["Art", "ificial", "Intel", "ligence", "is", "trans", "formative"].The number of tokens in an input directly affects inference costs because each token must be processed individually. More tokens mean higher computational demands and longer inference times. This has significant implications for:1. Cost per transaction: Larger token sizes increase the number of operations required for processing, directly impacting GPU usage and energy consumption.2. Latency: Real-time applications, such as chatbots, require rapid inference. Excessive token counts can delay responses, impacting user experience.<h3>Context Windows</h3>The context window refers to the maximum number of tokens that a model can process at once. This window determines how much prior information the model can "remember" when generating a response or prediction. Larger context windows enable the model to consider more extensive histories or contexts, improving the quality of outputs, especially in complex tasks like summarization or code generation.However, larger context windows significantly increase inference costs due to:1. Quadratic Scaling of Attention Mechanisms: In transformer-based models, the attention mechanism calculates relationships between every pair of tokens in the context window. If the context window is 4,000 tokens, the attention computation scales as 4,000^2, or 16 million operations, for a single layer. Expanding the window to 8,000 tokens results in 64 million (8,000^2) operations—quadrupling the computational demand.2. Memory Footprint: Larger context windows require more memory to store intermediate computations, which can strain hardware and lead to higher costs.Other important considerations are model complexity and hardware utilization. Larger models with more parameters can capture intricate patterns in data but demand more computational power during inference, leading to higher operational costs. Also, inference can be performed on various hardware, including CPUs, GPUs, and specialized accelerators. While GPUs offer parallel processing capabilities that can accelerate inference, they also consume significant energy, contributing to operational expenses.<h3>Financial and Environmental Costs</h3>The financial cost of inference encompasses expenses related to hardware acquisition, maintenance, and energy consumption. Energy costs are particularly significant, as continuous inference operations can lead to substantial electricity usage. For example, increasing the context window requires more powerful GPUs, higher memory capacity, and more energy to process data. Expanding context windows from 4,096 tokens to 16,384 tokens, as some cutting-edge models offer, can dramatically inflate costs. Beyond financial implications, the environmental impact of AI inference is garnering attention. For example, larger token sizes and extended context windows also demand higher energy consumption, exacerbating the environmental impact of AI. Each additional token processed adds to the overall carbon footprint, especially in high-demand use cases like chatbots, summarization tools, or generative design applications. Data centers hosting AI models consume large amounts of electricity, often sourced from non-renewable energy, leading to increased carbon emissions. A <a data-mce-href="https://www.wired.com/story/true-cost-generative-ai-data-centers-energy/" href="https://www.wired.com/story/true-cost-generative-ai-data-centers-energy/" target="_blank" rel="noopener">study</a> highlighted that AI operations require enormous energy, largely powered by nonrenewable resources, and substantial freshwater, causing strain on local communities. Furthermore, the <a data-mce-href="https://knowledge.wharton.upenn.edu/article/the-hidden-cost-of-ai-energy-consumption/" href="https://knowledge.wharton.upenn.edu/article/the-hidden-cost-of-ai-energy-consumption/">power required for inference can account for up to 60% of AI's total energy consumption</a>, underscoring the need for efficient inference mechanisms.<h3>Balancing Token Size and Context Windows Costs:</h3>We should be putting in clear strategies not only to accelerate our AI usage, but also how we will curb the costs of AI. To address the financial and environmental costs associated with AI inference, consider the following strategies: While larger token sizes and context windows improve model capabilities, they must be balanced against operational constraints. Some strategies include:- Dynamic Context Windows: Adjust the size of the context window based on the task. For instance, a smaller window may suffice for short conversations, while longer documents or technical tasks may benefit from an expanded window.- Efficient Tokenization: Use tokenizers that minimize the number of tokens required to represent the input while retaining semantic fidelity. For instance, OpenAI’s tokenizer is optimized for compressing common patterns of English text.- Sliding Windows for Context: When handling long inputs that exceed the model's context window, use sliding or overlapping windows to divide the input. This ensures the model processes the entire input, albeit in chunks, without overwhelming resources.Recent innovations aim to mitigate the resource demands of large token sizes and context windows. While some of these strategies are not yet widely adopted, they could put large dents in our growing inference costs. - Sparse Attention Mechanisms: Research into sparse transformers reduces the number of attention calculations by focusing on the most relevant tokens, significantly lowering computational overhead.- Memory-Augmented Models: Instead of processing entire contexts at once, these models store summaries or embeddings of past contexts, retrieving relevant information on demand.- Hardware Accelerators: Specialized hardware, such as TPUs (Tensor Processing Units), is optimized for the large matrix multiplications involved in processing tokens, improving efficiency.To conclude, understanding the intricacies of AI inference is crucial for managing the operational costs and environmental impact of generative AI systems. Organizations should carefully evaluate their use cases and adopt strategies to balance performance with resource utilization. Token size and context windows are fundamental to the operation and cost of generative AI models. While expanding these capabilities improves performance, it comes at a steep cost—financially, temporally, and environmentally. By optimizing models, efficiently managing context windows and token sizes, and adopting sustainable practices, organizations can harness the benefits of AI while minimizing financial expenditures and ecological footprints.

Inference: Understanding the Cost of Generative AI

As we drive towards data-driven decision-making, organizations are amassing vast amounts of data. However, the value of this data is often diminished by its lack of explainability. Without proper documentation, naming conventions, and context, analysts and engineers spend more time deciphering datasets than deriving insights. Making data intuitive and accessible is critical, especially as organizations increasingly rely on analytics and AI. Here’s how technical practices can enhance data explainability and make your organization’s data more usable and actionable.<h3>The Foundation: Documentation and Metadata</h3>The first step in making data more explainable is maintaining thorough technical documentation. Every dataset, table, and column in your organization’s ecosystem should have an associated record that answers fundamental questions:<ul style="list-style-type: circle;" data-mce-style="list-style-type: circle;"><li>What does this data represent?</li><li>Where did it come from?</li><li>How is it updated, and how frequently?</li><li>What are the constraints, transformations, and anomalies to be aware of?</li></ul>This documentation should include metadata such as data types, units of measurement, lineage (how data flows from source to destination), and usage examples. To keep this information accessible and up to date, consider storing it in a code repository, such as GitHub. By integrating your metadata into version-controlled repositories, you can:<ul style="list-style-type: circle;" data-mce-style="list-style-type: circle;"><li>Track changes to schemas and definitions over time.</li><li>Ensure teams across the organization have a single source of truth.</li><li>Automate synchronization with CI/CD pipelines to reflect updates in real time.</li></ul>For larger organizations, leveraging tools like DataHub, Amundsen, or Collibra can centralize metadata management and offer search-friendly interfaces. These platforms help users discover, understand, and trust data assets quickly.<h3>Intuitive Naming Conventions</h3>A well-documented dataset is of little use if its elements are cryptic or poorly named. Intuitive naming conventions are essential to making data understandable at a glance. Here are a few guidelines:1. Use descriptive and consistent names for tables, columns, and variables. For example, instead of naming a column col1, use customer_age to clarify its purpose.2. Avoid abbreviations unless they are universally understood within your organization.3. Adopt a consistent casing style across datasets. Common formats being snake_case or camelCase. It is best practice to pick one early in the process and enforce it for consistency accross the organization. 4. Prefix or suffix names with context where appropriate. For instance, use sales_region instead of region to differentiate it from other regional datasets.5. Document naming conventions in your metadata repository to ensure consistency across teams and projects.&nbsp;This will depend on your organization's size, however at a minimum the data documentation should exist in your code repository along with your other technical documents.&nbsp;&nbsp; <h3>Create Data Dictionaries and Cheat Sheets</h3>Building a data dictionary for every dataset is another powerful method of adding explainability. A data dictionary lists all fields in a dataset, along with their: Definitions, the data class (PI, SPI, etc.), Data types, example values OR list of allowed explicit values. While these metrics are well known there are some technical documentations that should be captured as well:&nbsp; <ul><li>What makes a record unique. This will tell users how to accurately count the number or records as there might not always be one unique identifying key. This will also be key to coding logic to iterate through all records. </li><li>Timestamps for each record documenting the create time or last update time. Not only is this important for compliance and auditability, but it is also essential to make sure you are making use of recent and relevant data. Noting this timestamp will also allow for the automation of retention policies so data can be removed from the system as defined by and organizations retention policy. </li><li>Relation to other data objects. How one object can join or look-up to another is essential. This will help analytics developers and AI professionals alike as a data object is often not useful when looked at in isolation.</li></ul>Cheat sheets or quick-reference guides for common datasets or processes can also reduce onboarding time for new team members and streamline collaboration. <h3>Standardized Data Formats and Units</h3>Consistency in formats and units of measurement is crucial for reducing confusion. For instance:<ul style="list-style-type: circle;" data-mce-style="list-style-type: circle;"><li>Use standardized date formats (e.g., ISO 8601: YYYY-MM-DD).</li><li>Ensure numeric fields use consistent units (e.g., dollars instead of mixing cents and dollars).</li><li>Clearly define categorical variables, such as coding gender as M/F or male/female, but not both.</li></ul>These standards should be outlined in your documentation and enforced via validation rules in data pipelines. These are also important to enforce to make sure all data is in the right format to reduce the amount of data cleaning later. The ability to automate or do large scale data processing depends on your data being in a predicable format. <h3>Steps for Reducing Time Spent on Data Preparation</h3>The ultimate goal of explainable data is to reduce time spent on data gathering, cleaning, and preparation. By integrating the practices above with automation, you can streamline workflows:1. Automate metadata collection and lineage tracking as part of your ETL/ELT pipelines.2. Use schema validation tools to ensure datasets conform to expected structures.3. Implement data quality checks to flag anomalies or inconsistencies early.<h3>Explainability for AI and Analytics</h3>Explainability is especially critical for analytics and AI initiatives, where trust in data is paramount. Models are only as good as the data they’re trained on, and a lack of transparency can lead to biases or errors. By making data explainable, organizations can:<ul style="list-style-type: circle;" data-mce-style="list-style-type: circle;"><li>Increase confidence in analytics and AI outputs.</li><li>Accelerate experimentation and prototyping by reducing ambiguity.</li><li>Enable cross-functional teams to collaborate more effectively.</li></ul>To start, teams can rely on GitHub to version control metadata files, enforce naming conventions through code reviews, and document datasets in Markdown files alongside their repositories.Overall, adding explainability to your data isn’t just a best practice—it’s a necessity for modern organizations striving to make data-driven decisions. By maintaining proper documentation, adopting intuitive naming conventions, and leveraging metadata management tools, you can ensure your data is clear, consistent, and actionable. These efforts not only reduce time spent on data preparation but also build a strong foundation for analytics and AI initiatives, empowering your organization to focus on insights rather than cleanup.

Adding Explainability to your Data

a bunch of blue wires connected to each other

As part 2 of our "Getting into the Weeds of Database Storage" series, let's take a closer look at table spaces. When managing a database, understanding how data is stored and accessed can make a significant difference in performance and scalability. One of the most critical components of database storage is the use of table spaces. Often overlooked, table spaces play a fundamental role in database optimization and understanding them can help you design better storage configurations.<h3>What Are Table Spaces?</h3>A table space is a logical layer within a database that maps to physical storage locations on disk. It acts as a container or storage area for database objects like tables, indexes, and large objects (LOBs). By organizing data within table spaces, administrators can control where and how data is stored, improving performance, reliability, and maintainability. In most databases, a table space is linked to one or more physical storage files or devices. This abstraction allows for more granular control over storage resources, such as allocating different storage types (e.g., SSDs, HDDs) for different workloads.Note - While table spaces are an important tool, custom table space configuration is not always required and sometimes can be overkill for simple databases. Most databases have a default tablespace configured so users do not have to always configure this. For smaller databases or for simple solutions table space configuration can be something to skip until it's actually needed.&nbsp;<h3>How Should Table Spaces Be Configured?</h3>Configuring table spaces effectively requires thoughtful planning based on your database workload and storage infrastructure. Here are some best practices:1. Separate Data and Indexes: By placing tables and their indexes in separate table spaces, you can reduce I/O contention and improve query performance. For example, if both are accessed simultaneously during a query, this separation allows reads and writes to occur in parallel.2. Use Different Storage for Different Workloads: Place frequently accessed or performance-critical data on faster storage like SSDs. Store archival or less frequently accessed data on slower, cheaper storage like HDDs.3. Allocate Table Spaces Based on Usage Patterns: Use separate table spaces for transactional and analytical workloads. Consider creating dedicated table spaces for large objects (LOBs) to isolate their performance impact.4. Plan for Growth: Ensure table spaces have sufficient capacity to handle future data growth. Consider enabling auto-extension for table spaces where supported to prevent unexpected storage constraints.&nbsp;5. Distribute I/O: By spreading table spaces across multiple disks or storage arrays, you can distribute I/O load more evenly, reducing bottlenecks and improving throughput.6. Backups and Maintenance: Configure table spaces to simplify backup and recovery. For instance, critical data can be placed in dedicated table spaces that are backed up more frequently than non-essential data.<h3>Optimizing Databases with Table Spaces</h3>Table spaces enable fine-grained control over storage, leading to better performance and resource utilization. Here’s how you can use them to optimize your database:Improve Query Performance: By isolating frequently queried tables or indexes in high-performance table spaces, you can reduce query latency.Minimize Fragmentation: Regularly reorganizing table spaces can help minimize fragmentation, ensuring that data retrieval remains fast.Prioritize Critical Workloads: Assigning separate table spaces for critical workloads ensures that their performance isn't impacted by less important processes.Enable Parallelism: Distributing table spaces across multiple devices allows multiple operations to execute concurrently, taking advantage of available I/O bandwidth.<h3>How Table Spaces Are Used in Db2 and PostgreSQL</h3>In IBM Db2, table spaces are a core component of storage architecture. Db2 supports three main types of table spaces:1. SMS (System-Managed Space): The operating system manages the physical storage. These are simpler to configure but provide less control over storage allocation.2. DMS (Database-Managed Space): The database manages the storage, allowing for greater control and optimization. You can allocate storage explicitly and manage growth proactively.3. Automatic Storage: Db2 can manage table spaces automatically, allocating and resizing storage as needed. This combines the simplicity of SMS with the flexibility of DMS.Db2 allows administrators to create specific table spaces for indexes, temporary data, and LOBs, making it easier to optimize performance for diverse workloads.In PostgreSQL, table spaces provide a mechanism to define locations in the file system where data files are stored. Unlike DB2, PostgreSQL table spaces are always user-defined and are not tied to storage types.<ul><li>PostgreSQL allows users to create custom table spaces using the CREATE TABLESPACE command.</li><li>Table spaces can be assigned to individual databases or even specific tables and indexes, offering fine-grained control over data placement.</li><li>Administrators can use symbolic links to map table spaces to different storage devices, enabling flexible storage configurations.</li></ul>PostgreSQL’s approach to table spaces is straightforward yet powerful, offering enough flexibility to isolate high-usage tables, optimize storage costs, and manage data growth effectively.<h3>Final Thoughts</h3>Table spaces are an important tool for managing database storage efficiently. Properly configuring and utilizing table spaces can enhance performance, reduce I/O contention, and provide flexibility for future growth. While every database system implements table spaces differently, understanding their role in systems like DB2 and PostgreSQL can provide insights into making the most of your storage infrastructure. Whether you’re dealing with transactional systems or large-scale analytics, taking the time to design an effective table space strategy can pay off significantly in terms of performance and reliability. Stay tuned for our next storage topic in part 3!

Pt. 2 Getting into the Weeds of Database Storage: Table Spaces

an abstract painting of blue and red colors

As AI continues to evolve, the need for vast amounts of data to train and update models has never been greater. One emerging solution to meet this demand is synthetic data—data that is artificially generated rather than obtained from real-world activity. While synthetic data offers promising benefits, it also presents significant challenges. In this blog post, let's explore the upside and potential pitfalls of synthetic data, particularly in the context of AI training, to understand its potential and the hurdles we must overcome.<h3>The Possibilities of Synthetic Data</h3>There are many use cases where synthetic data will propel success and advancement. In AI and machine learning, it facilitates training models where real data is scarce, such as in autonomous vehicles and healthcare, allowing for safe simulation of rare conditions and safeguarding individual privacy. For software development, synthetic data enables robust stress testing and performance optimization without risking exposure of sensitive production data. Additionally, synthetic data addresses imbalanced datasets, by improving the detection of rare events like fraud and underrepresented conditions. In education, synthetic data serves as a practical tool for data science students and professional training, offering realistic datasets for hands-on learning. These diverse use cases underscore synthetic data's potential to drive innovation and improve outcomes across multiple fields. No matter the use case, synthetic data brings the following benefits:Unlimited Data Generation: One of the most compelling advantages of synthetic data is its ability to be generated in virtually unlimited quantities. Traditional data collection methods are often time-consuming and resource-intensive, making it difficult to keep up with the rapid pace of AI development. Synthetic data provides a scalable solution, allowing for continuous and instantaneous creation of new datasets. This capability is crucial for keeping AI models up to date in a world where data changes quickly.Enhanced Privacy: Using real data often raises significant privacy concerns, especially when it involves personal information. Synthetic data, being artificially generated, can mimic the statistical properties of real data without containing any actual personal information. This makes it an excellent option for training AI models while ensuring compliance with privacy regulations like GDPR and CCPA. It also minimizes the risk of data breaches and misuse of sensitive information. Improved Testing and Development: In software development, especially when building systems at scale, having access to production-like data is invaluable for testing. Synthetic data can be tailored to resemble real-world scenarios, enabling developers to stress-test and optimize their systems in non-production environments. This ensures that applications can handle real-world conditions without the risks associated with using actual production data in lower environments.<h3>Proceeding with Caution </h3>Risk of Bias: One of the significant drawbacks of synthetic data is the potential for inherent biases. If the algorithms generating synthetic data are trained on biased real-world data, the synthetic data will likely perpetuate those biases. This can lead to AI models that reinforce existing prejudices and inequities, making it essential to develop methods to detect and mitigate bias in synthetic data. What complicates this problem farther, is that it is nearly impossible to detect if there is an issue until we have implemented this over a long period of time. &nbsp;For example, if a real-world dataset used to generate synthetic data has gender or racial biases, the resulting AI models might exhibit similar biases in their predictions and decisions. Addressing this requires developing robust methods for detecting and mitigating bias in both the real-world data and the synthetic data generation processes.Validation Challenges: Validating synthetic data to ensure it is sufficiently realistic and representative of actual conditions is another major challenge. While synthetic data can be designed to mimic real data, there is always the question of whether it is "real enough." Without rigorous validation, there is a risk that AI models trained on synthetic data may perform poorly when exposed to real-world data, leading to unreliable or inaccurate predictions. Developing effective validation techniques is crucial to ensure the reliability and accuracy of AI models trained on synthetic data. Security Concerns: The security of synthetic data is another critical concern. Once an AI model is trained on a dataset, the information contained within that data becomes ingrained in the model. If the synthetic data includes malicious or erroneous information, it can have long-lasting negative effects on the model's performance. Unlike traditional software bugs that can be fixed through updates, correcting issues in an AI model often requires retraining it from scratch, which can be resource-intensive. Ensuring the integrity of synthetic data and protecting it from malicious manipulation is essential to prevent harmful impacts on AI systems. Management and Scalability Issues:&nbsp;While the ability to generate synthetic data at scale is a significant advantage, it also presents substantial management challenges. The infrastructure and processes required to create, validate, and maintain synthetic data at the scale needed for modern AI applications are still in their infancy. Many organizations may lack the expertise and resources to effectively manage synthetic data at scale, leading to potential inefficiencies and risks. Additionally, ensuring that synthetic data is continuously updated to reflect changing real-world conditions requires ongoing investment and development. Organizations must develop robust data management strategies and invest in the necessary infrastructure to handle the complexities associated with large-scale synthetic data generation and maintenance.<h3>The Future of Synthetic Data</h3>Synthetic data holds great promise for the future of AI training, offering solutions to some of the most pressing data challenges. Its ability to generate unlimited data, enhance privacy, and improve testing environments makes it a valuable tool in the AI developer's arsenal. However, the risks associated with bias, validation, security, and management cannot be overlooked.As we move towards greater reliance on synthetic data, it is crucial to invest in robust frameworks and best practices for generating, validating, and securing this data. By addressing these challenges head-on, we can harness the full potential of synthetic data while minimizing its risks, paving the way for more accurate, fair, and reliable AI systems.In conclusion, synthetic data is likely to play an essential role in the future of AI, but careful consideration and preparation are necessary to manage it effectively at scale. As we navigate this new frontier, ongoing research and collaboration will be key to overcoming the hurdles and unlocking the benefits synthetic data has to offer.By Ellie Najewicz

DataSphereChronicle

Blog

Evolving to Meet the Demands of AI

Iceberg and other Data Warehousing Technologies

Inference: Understanding the Cost of Generative AI

Adding Explainability to your Data

Pt. 2 Getting into the Weeds of Database Storage: Table S...

Two Sides of Synthetic Data