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Articles by Frank A. Banin
In a landscape abundant with AI innovations and promises, a crucial question arises: "Is leveraging SQL and structured data genuinely beneficial for your business in today's environment?" SQL and structured data have been pivotal in shaping contemporary data architectures, including Lakehouse and Medallion architectures, as well as distributed computing frameworks like Spark and MPPs. These systems are vital for large-scale data processing and play a significant role in business intelligence...
DAX For SQL Folks: Part I- Intro to DAX, Power BI and Data Viz – SQLServerCentral
Introduction In response to my approach in the popular MDX Guide for SQL Folks series, I am going to try and repeat the same style of learning for the Data Analysis Expression(DAX) language. I am going to use SQL as a good frame of reference for starting or developing a new approach for improving your DAX learning experience. This is useful for developers starting to learn the DAX language to more advanced developers who have struggled to adjust to the language coming from a SQL background.
SQL Server Integrates Hadoop and Spark out-of-the box: The Why? – SQLServerCentral
Introduction Microsoft announced in September 2018 that SQL Server 2019, which is now in preview, will have a Big Data Cluster deployment option. This is a Big-Data-capable SQL Server with elastic scale and extended Artificial Intelligence (AI) capabilities, mostly as a result of deep integration of Hadoop and Spark out-of-the-box. The new SQL Server Big Data Cluster is expected to yield a lot more than the ability to employ Hadoop and Spark directly from a SQL Server environment.
DAX for SQL Folks: Part III - DAX Calculations – SQLServerCentral
Introduction In response to my approach in the popular MDX Guide for SQL Folks series. I am using SQL as a good frame of reference for starting or developing a new approach for improving your Data Analysis Expression(DAX) language learning experience in this series. This is useful for developers starting to learn the DAX language to more advanced developers who have struggled to adjust to the language coming from a SQL background.
DAX for SQL Folks: Part II - Translating SQL Queries to DAX Queries – SQLServerCentral
Introduction In response to my approach in the popular MDX Guide for SQL Folks series, I am using SQL as a good frame of reference for starting or developing a new approach for improving your Data Analysis Expression(DAX) language learning experience. This is useful for developers starting to learn the DAX language to more advanced developers who have struggled to adjust to the language coming from a SQL background.
Distributed Computing Principles and SQL-on-Hadoop Systems – SQLServerCentral
Introduction In a recent SQL-on-Hadoop article on Hive ( SQL-On-Hadoop: Hive-Part I), I was asked the question "Now that Polybase is part of SQL Server, why wouldn't you connect directly to Hadoop from SQL Server? " My simple answer will be "Because of big data storage and computation complexities".
SQL-On-Hadoop: Hive - Part II – SQLServerCentral
Introduction All big data systems are not made equal, including SQL-on-Hadoop systems like Hive, Polybase and SparkSQL. All of these employ various distributed computing techniques. In this article we learned why it is critical to understand some of these techniques in other to make the right big data tool selections. In Part I of this series we learnt what gave rise to Hadoop and the need for SQL-on-Hadoop big data systems which was pioneered by Hive.
SQL-On-Hadoop: Hive - Part I – SQLServerCentral
Introduction In this new SQL-On-Hadoop series of articles, we will continue exploring the benefits of distributed computing using your SQL Skillsets. We will learn how to run interactive and batch SQL queries on structured and semi-structured Big Data stored on Hadoop and other file storage systems using HiveQL,T-SQL and other Structured query options like Spark SQL.
ETL Performance Auditing - Part 1: Introduction to ETL Auditing – SQLServerCentral
Introduction You are a SQL Developer or BI architect, and you get news that your scheduled production ETL Job(s) that were running “perfectly” are taking “longer” and “longer” to run. The problem is there are over 300 individual child SSIS packages that stage data, load a Data Warehouse (WH), load Data Marts, process SSAS Cubes and manage your Database and Cube Partitions. Some of the questions that could be asked would be: What is “longer”, or what is the benchmark for that complaint?
Advanced Analytics with R & SQL: Part I - R Distributions – SQLServerCentral
Introduction SQL Server currently has some very powerful and cool features, but the integration of R (the most used Analytics, data mining and data science software) into SQL Server is epic. The hope is that developers, analysts, data scientists, businesses and enterprises will begin to grasp the potential this offers. I first published the article Data Science for SQL Folks: Leveraging SQL and R on 11/03/2014.
Advanced Analytics with R and SQL Part II - Data Science Scenarios – SQLServerCentral
Introduction In Part I we learned why Microsoft integrated R and SQL Server. We also learned about the three major R distributions (OSR, MRO and MRS) available today and also their capabilities. In this part of the series, we will look at some context under which the R distributions could be used selectively by a data scientist to build a predictive model.
Hadoop For SQL Folks: Architecture, MapReduce and Powering IoT – SQLServerCentral
Introduction Today Hadoop still remains the dominant technology for storing and processing Big Data as I explained in this article two years ago. With its continued growth in scope and scale of analytics applications in its ecosystem, it is also pioneering a new enterprise data architecture referred to as Data Lake that seeks to integrates enterprise Hadoop, enterprise data warehouse (EDW) and other existing data systems.
Data Science for SQL Folks: Leveraging SQL and R – SQLServerCentral
Introduction With a new drive to move beyond the myopic traditional analytics to higher value driven Business Analytics (Analytics), one often requires more than one tool, development environment and analytic techniques to accomplish these endeavors. The figure below indicates that, the higher the value you derive, the more difficult the analytics involved. This difficulty is known to be cumulative and comes from the fact that the higher the value the more tools you might need.
ETL Performance Auditing - Part 3: Auditing Data Warehouse Phase – SQLServerCentral
Introduction You are a SQL Developer or BI architect, and you get news that your scheduled production ETL Job(s) that were running “perfectly” are taking “longer” and “longer” to run. The problem is there are over 300 individual child SSIS packages that stage data, load a Data Warehouse (WH), load Data Marts, process SSAS Cubes and manage your Database and Cube Partitions. Some of the questions that could be asked would be: What is “longer”, or what is the benchmark for that complaint?
ETL Performance Auditing - Part 2: Auditing Data Staging Phase – SQLServerCentral
Introduction You are a SQL Developer or BI architect, and you get news that your scheduled production ETL Job(s) that were running “perfectly” are taking “longer” and “longer” to run. The problem is there are over 300 individual child SSIS packages that stage data, load a Data Warehouse (WH), load Data Marts, process SSAS Cubes and manage your Database and Cube Partitions. Some of the questions that could be asked would be: What is “longer”, or what is the benchmark for that complaint?
MDX Guide for SQL Folks: Part I - Navigating The Cube – SQLServerCentral
There are a lot of Multidimensional Expressions (MDX) resources available, but most teaching aids out there are geared towards professionals with cube development experience. As a result SQL developers with no cube development experience start learning MDX on a poor footing because many of the learning aids completely disregard SQL as a good frame of reference for starting the learning process.
MDX Guide for SQL Folks: Part III - Advanced Analysis – SQLServerCentral
Introduction In Part I and Part II we covered some key MDX and cube concepts necessary for writing MDX even if you don’t have any cube development experience. We will cotinue the series by looking at some important MDX features and topics necessary for writing advanced MDX queries. Please refer to Part I for the prerequisites needed to execute all MDX queries in this series.
MDX Guide for SQL Folks: Part II - Hierarchies and Functions – SQLServerCentral
Introduction This is a continuation of the series to introduce some core MDX and cube concepts by first drawing on the similarity and differences between MDX and SQL. In Part I we looked at how tables translate directly into to cube dimensions. We also looked at how MDX is different or similar to SQL, by looking at both in the context of the basic SQL SELECT statement. For example we saw why all three queries in listing1 below yield the same result.
Serving Warm SSIS Errors – SQLServerCentral
Troubleshooting SSIS errors can be challenging in themselves, so retrieving the correct error messages should not be, especially in time-sensitive production environments. I am going to demonstrate how to set up and always deliver the correct, well-formatted SSIS errors when a Package fails. This may save you or anyone the trouble of digging around in Event logs and SQL Agent Jobs for the right SSIS error messages.
Managing Data Dictionaries in DW and DSS Database Applications – SQLServerCentral
Introduction In this article I am going to demonstrate how Data Dictionaries, which normally hold key business information slated for BI Applications, can be managed and deployed in Data Warehouse (DW) and Decision Support System (DSS) database applications. First, I will show how this metadata can be uploaded to a DW or Datamarts as SQL Server Extended Properties and secondly, how it can be propagated from Extended Properties and tables into custom reports on SSMS and SSRS.
Managing Data Removal Using SSIS – SQLServerCentral
Introduction In volatile database environments, data removal is often required during recurring full load ETL processes that populate these databases. This presents challenges especially in very large databases that utilize referential integrity (RI) and indexed views. When RI is turned on, data removal must follow a certain order, making regular truncations and deletion frustrating, and nearly impossible.
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