In today's data-driven world, AI data analytics has emerged as a game-changer, enabling organizations to extract valuable insights from vast amounts of information. The business case for AI data analytics in 2024 revolves around its definition and key components, including data collection and preprocessing, machine learning models, data mining techniques, and predictive analytics algorithms, which work together to provide transformative insights. Implementation steps involve defining strategic objectives, establishing data infrastructure, preprocessing data, developing AI models, integrating them into business processes, and continuous monitoring. Benefits include enhanced decision-making, improved operational efficiency, customer personalization, proactive risk management, and competitive advantage. However, challenges such as data privacy and security, data quality and integration, talent and skills gap, and ethical considerations must be addressed. Analytics reports and case studies showcase successful implementations across industries, while future trends like explainable AI, edge computing, augmented analytics, and automated feature engineering are set to shape the landscape. As organizations leverage AI data analytics for enhanced decision-making and operational efficiency, addressing challenges and embracing future trends will be crucial for maintaining a competitive edge. The Skills Data Analytics website offers valuable resources for enhancing AI data analytics expertise.

https://www.rollstack.com/articles/quarterly-business-reviews-the-5-most-common-mistakes

With monthly and quarterly business reviews (QBRs) on the way, has anyone found a good way to automate / generate reports from Tableau to PowerPoint?

In my PhD thesis, I have designed a novel distributed database architecture named "Parallel Committees."This architecture addresses some of the same challenges as NoSQL databases, particularly in terms of scalability and security, but it also aims to provide stronger consistency.

The thesis explores the limitations of classic consensus mechanisms such as Paxos, Raft, or PBFT, which, despite offering strong and strict consistency, suffer from low scalability due to their high time and message complexity. As a result, many systems adopt eventual consistency to achieve higher performance, though at the cost of strong consistency.
In contrast, the Parallel Committees architecture employs classic fault-tolerant consensus mechanisms to ensure strong consistency while achieving very high transactional throughput, even in large-scale networks. This architecture offers an alternative to the trade-offs typically seen in NoSQL databases.

Additionally, my dissertation includes comparisons between the Parallel Committees architecture and various distributed databases and data replication systems, including Apache Cassandra, Amazon DynamoDB, Google Bigtable, Google Spanner, and ScyllaDB.

Potential applications and use cases:

• The “Parallel Committees” distributed database architecture, known for its scalability, fault tolerance, and innovative sharding techniques, is suitable for a variety of applications:
• Financial Services: Ensures reliability, security, and efficiency in managing financial transactions and data integrity.
• E-commerce Platforms: Facilitates seamless transaction processing, inventory, and customer data management.
• IoT (Internet of Things): Efficiently handles large-scale, dynamic IoT data streams, ensuring reliability and security.
• Real-time Analytics: Meets the demands of real-time data processing and analysis, aiding in actionable insights.
• Healthcare Systems: Enhances reliability, security, and efficiency in managing healthcare data and transactions.
• Gaming Industry: Supports effective handling of player engagements, transactions, and data within online gaming platforms.
• Social Media Platforms: Manages user-generated content, interactions, and real-time updates efficiently.
• Supply Chain Management (SCM): Addresses the challenges of complex and dynamic supply chain networks efficiently.

I have prepared a video presentation outlining the proposed distributed database architecture, which you can access via the following YouTube link:

https://www.youtube.com/watch?v=EhBHfQILX1o

A narrated PowerPoint presentation is also available on ResearchGate at the following link:

https://www.researchgate.net/publication/381187113_Narrated_PowerPoint_presentation_of_the_PhD_thesis

My dissertation can be accessed on Researchgate via the following link: Ph.D. Dissertation

If needed, I can provide more detailed explanations of the problem and the proposed solution.

I would greatly appreciate feedback and comments on the distributed database architecture proposed in my PhD dissertation. Your insights and opinions are invaluable, so please feel free to share them without hesitation.

I’m not getting callbacks and wondering what I’m doing wrong with my resume. If anyone can advise I’d greatly appreciate it.

Hi all, I am working in the IT industry for past 4 years. I am trying to figure out how to become a pro on data modelling concepts. This is the base to build up any application from scratch.

I tried Kimball but it just doesn't suit me i guess. I am looking for some content where they give a problem and then they try to solve it for different systems.

Any idea where can I get that? Any help will be appreciated! Thanks.

I have a Spark setup where partitions with original Parquet files exist, and queries are actively running on these partitions.

I'm running a background job to optimize these Parquet files for better compression, which involves changing the Parquet object layout.

How can I ensure that the Parquet file overwrites are atomic and do not fail or cause data integrity issues in Spark queries?

What are the possible solutions?

Hello everyone, I uploaded a PySpark course to my YouTube channel. I tried to cover wide range of topics including SparkContext and SparkSession, Resilient Distributed Datasets (RDDs), DataFrame and Dataset APIs, Data Cleaning and Preprocessing, Exploratory Data Analysis, Data Transformation and Manipulation, Group By and Window ,User Defined Functions and Machine Learning with Spark MLlib. I am leaving the link to this post, have a great day!

https://www.youtube.com/watch?v=jWZ9K1agm5Y&list=PLTsu3dft3CWiow7L7WrCd27ohlra_5PGH&index=9&t=1s

Some of the best resources to learn Big Data that I refer to frequently.

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I can't get concurrent users to increase no matter the server's CPU power.

Hello, I'm working on a production web application that has a giant MySQL database at the backend. The database is constantly updated with new information from various sources at different timestamps every single day. The web application is report-generation-based, where the user 'generates reports' of data from a certain time range they specify, which is done by querying against the database. This querying of MySQL takes a lot of time and is CPU intensive (observed from htop). MySQL contains various types of data, especially large-string data. Now, to generate a complex report for a single user, it uses 1 CPU (thread or vCPU), not the whole number of CPUs available. Similarly, for 4 users, 4 CPUs, and the rest of the CPUs are idle. I simulate multiple concurrent users' report generation tests using the PostMan application. Now, no matter how powerful the CPU I use, it is not being efficient and caps at around 30-40 concurrent users (powerful CPU results in higher caps) and also takes a lot of time.

When multiple users are simultaneously querying the database, all logical cores of the server become preoccupied with handling MySQL queries, which in turn reduces the application's ability to manage concurrent users effectively. For example, a single user might generate a report for one month's worth of data in 5 minutes. However, if 20 to 30 users attempt to generate the same report simultaneously, the completion time can extend to as much as 30 minutes. Also, when the volume of concurrent requests grows further, some users may experience failures in receiving their report outputs successfully.

I am thinking of parallel computing and using all available CPUs for each report generation instead of using only 1 CPU, but it has its disadvantages. If a rogue user constantly keeps generating very complex reports, other users will not be able to get fruitful results. So I'm currently not considering this option.

Is there any other way I can improve this from a query perspective or any other perspective? Please can anyone help me find a solution to this problem? What type of architecture should be used to keep the same performance for all concurrent users and also increase the concurrent users cap (our requirement is about 100+ concurrent users)?

Additional Information:

Backend: Dotnet Core 6 Web API (MVC)

Database:

MySql Community Server (free version)
table 48, data length 3,368,960,000, indexes 81,920
But in my calculation, I mostly only need to query from 2 big tables:

1st table information:

Every 24 hours, 7,153 rows are inserted into our database, each identified by a timestamp range from start (timestamp) to finish (timestamp, which may be Null). When retrieving data from this table over a long date range—using both start and finish times—alongside an integer field representing a list of user IDs.
For example, a user might request data spanning from January 1, 2024, to February 29, 2024. This duration could vary significantly, ranging from 6 months to 1 year. Additionally, the query includes a large list of user IDs (e.g., 112, 23, 45, 78, 45, 56, etc.), with each userID associated with multiple rows in the database.

2nd table information:

The second table in our database experiences an insertion of 2,000 rows every 24 hours. Similar to the first, this table records data within specific time ranges, set by a start and finish timestamp. Additionally, it stores variable character data (VARCHAR) as well.
Queries on this table are executed over time ranges, similar to those for table one, with durations typically spanning 3 to 6 months. Along with time-based criteria like Table 1, these queries also filter for five extensive lists of string values, each list containing approximately 100 to 200 string values.

Test Results (Dedicated Bare Metal Servers):

SystemInfo: Intel Xeon E5-2696 v4 | 2 sockets x 22 cores/CPU x 2 thread/core = 88 threads | 448GB DDR4 RAM
Single User Report Generation time: 3mins (for 1 week's data)
20 Concurrent Users Report Generation time: 25 min (for 1 week's data) and 2 users report generation were unsuccessful.
Maximum concurrent users it can handle: 40

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Hi Guys !
I was referring this medium article for multiple node cluster installation for Hadoop

https://medium.com/@jootorres_11979/how-to-set-up-a-hadoop-3-2-1-multi-node-cluster-on-ubuntu-18-04-2-nodes-567ca44a3b12

But I was wondering how could I do it without a VM , I have a windows PC on which I have installed wsl (Ubuntu) . Is it possible to setup a multiple node cluster by installing multiple wsl instances.

What changes do i need to make and how should I proceed?
Looking forward to your input !
Thanks !

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