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SQL Vector Databases Are Shaping the New LLM and Big Data Paradigm

SQL Vector Databases Are Shaping the New LLM and Big Data Paradigm... Combining vector databases with SQL can provide the accuracy and performance required to build modern production-level GenAI applications.

SQL Vector Databases Are Shaping the New LLM and Big Data Paradigm #generativeai #llm #vectordatabases #sql #bigdata https://lnkd.in/gg_KbqKU

https://lnkd.in/eEcfnkdZ #SQL #Vector Databases Are Shaping the New #LLM and #BigData Paradigm

For semantically searching over structured (or semi-structured) data, one of the situations is your files contain both unstructured data and structured data. Then, an SQL-based #vectordatabase, like MyScale, is the perfect choice. ❓So what is an #SQL vector database? How does it work? 🔍Find the answer in our blog👇 https://lnkd.in/geVFZk8V

SQL Vector Databases Are Shaping the New LLM and Big Data Paradigm https://lnkd.in/gw6tUGDE #GenAi #llm #vectordatabase

Understanding SQL Concepts and Queries Structured Query Language (SQL) is the foundation of data management in relational databases. It allows users to create, read, update, and delete data within a database. In this blog, the wrtier explores key SQL concepts and queries, providing code examples to help you understand and apply these principles in your projects. https://lnkd.in/dZzK86pT #MVYL #AI #ArtificialIntelligence #SQL #SQLServer #Queries

ISO acknowledges that Graph Query Language (GQL), not SQL, is best suited for finding interconnected patterns in data and publishes a new standard. An critical milestone, as Graph databases are grounding database for Gen AI #neo4j #graph #genai

Bonjour!! This week, I had the opportunity to delve deeper into Apache Spark, a widely used platform for large-scale data analysis. The course material provided a comprehensive understanding of various data types supported by Spark and how to optimize their use in data analysis. One of the key topics covered was the different data types in Spark, including Booleans, Numbers, Strings, Dates, and Timestamps. Understanding these data types is crucial, especially when performing filtering and aggregation operations. For instance, Boolean data types can be used to construct effective conditional statements, while numeric data types are commonly used in counting and computational tasks. We also explored string data manipulation, a common task in data processing such as manipulating log files and using regular expressions for pattern substitution. Handling date and time data was another significant topic, focusing on the challenges of time zones and format consistency. Spark provides DateType and TimestampType to manage these challenges effectively. Additionally, we delved into complex data structures like Structs, Arrays, and Maps. This knowledge is valuable for organizing and structuring more intricate datasets according to specific analysis needs. Handling null values in datasets was another important lesson. Best practices include explicitly removing or filling null values using functions like `Dropna()` and `Fillna()` in DataFrames. Data aggregation was discussed in-depth, covering methods to summarize data from multiple observations into a single value, such as sum, average, and count. Grouping data based on categorical variables and calculating aggregate statistics for each group was a highlight of this week's material. Joining data from multiple sources was also a crucial topic, with various join types such as inner joins, outer joins, left outer joins, and right outer joins. Understanding these concepts is essential for combining information from different datasets based on common keys. Lastly, I learned about SparkSQL, which allows structured data processing using SQL or the DataFrame API. SparkSQL simplifies integration with various data sources and BI tools through JDBC or ODBC and offers optimization features through the Catalyst Optimizer and User-Defined Functions (UDFs). As a data enthusiast, I often face challenges in processing various data types from different datasets. This week, I applied my new knowledge by processing a large dataset using Spark. Utilizing SparkSQL made it much easier to run complex queries and combine data from multiple sources. This experience has enriched my understanding of the importance of selecting the right data types and efficient data processing methods. . #StudiIndependenBersertifikat #SIBDigitalSkola #DigitalSkola #DataEngineer