DEV Community: Kemal Cholovich

Razvoj Karijere kroz Google Developer Grupu: Moje Iskustvo kao Organizatora GDG Sarajevo

Kemal Cholovich — Fri, 26 Jul 2024 16:05:14 +0000

Pozdrav svima,

Kao lider/organizer GDG Sarajevo zajednice, imam priliku svakodnevno raditi s talentovanim ljudima koji su željni znanja i napretka. U zadnje vrijeme, primijetili smo priliv novih članova koji su izuzetno motivisani da unaprijede svoje vještine. Stoga bih želio podijeliti nekoliko smjernica koje će vam pomoći u razvoju karijere kroz našu Google Developer Grupu.

Put Učenja i Profesionalna Certifikacija

Da bismo postigli uspjeh, važno je da imamo jasnu putanju učenja. Na besplatnim radionicama, koje organiziramo svakog mjeseca, pored besplatnog senior mentorshipa, dobijate i besplatni pristup Googlovoj platformi za ucenje vrijednoj $300/per user. Zato dodajte gas i iskoristite priliku da u Sarajevu dobijete world class obuku za Cloud!

Malim koracima naprijed:

Preporučujem sljedeće programe koje bi svako trebao proći kako bi stekao vrijedno praktično iskustvo i bio spreman za profesionalne certifikate u narednih 1-3 godine:

Osnove Google Cloud Platforme:
- Cloud Digital Leader
Napredne Vještine u Cloud-u:
- Associate Cloud Engineer
Umjetna Inteligencija AI:

Nakon završetka ovih kurseva i sticanja mini certifikata, možete se pripremiti za profesionalnu certifikaciju prema vlastitim preferencijama (DevOps, Security, Solution Architecture, Machine Learning...). Više o tome možete pronaći ovdje: Google Cloud Certification.

Certifikati su sve više traženi od strane poslodavaca i definitivno će vam pomoći da se profilirate u industriji. Bez obzira na vaše prethodno iskustvo, ovi certifikati će vam otvoriti vrata ka sjajnoj karijeri. Preporučujem da svaki dan posvetite barem 60 minuta radu po ovom planu.

Karijera kroz GDG Sarajevo Zajednicu

Nakon što ste stekli profesionalne certifikate, otvaraju vam se brojne mogućnosti unutar Google Developer zajednice. Kao lider GDG Sarajevo, vidio sam kako članovi nakon certifikacije dobijaju preporuke i postaju Google Developers Expert. Ova titula donosi reputaciju, ali i priliku za gostovanja na događajima GDG zajednica širom svijeta, gdje Google pokriva troškove putovanja i smještaja(Slican program ima i Microsoft sa MVP titulom!).

Pored toga, gdje god da se relocirate ili dobijete novi posao, kao član GDG grupe imate priliku da se pridružite lokalnoj GDG grupi ili osnujete novu, svoju. Na taj način ćete uvijek biti povezani s globalnom mrežom profesionalaca i entuzijasta.

Mogući Ishodi Karijere

Na temelju iskustava naše grupe, evo nekoliko scenarija koji su realni i dostižni:

Rad kao konsultant za Google partnerske kompanije, Enterprise level kompanija;
Zapošljavanje u Google-u;
Razvoj vlastitog biznisa i partnerstvo s Google-om;
Razvoj karijere na drugim cloud platformama...;

Ovo su samo neki od puteva koje možete slijediti. Ključno je da iskoristite prilike koje vam se nude, radite na svom usavršavanju i ostanete aktivni član naše zajednice. Siguran sam da ćete uz posvećenost i rad postići svoje ciljeve i imati sjajnu karijeru. Karijera se gradi krvlju i znojem i taj se put zlatom ili dolarima ili coinsima sigurno vraca!

Za lenjivce nema mjesta!

Sretno svima i radujem se našim budućim uspjesima!

Srdačno,
Kemal Cholovich

Understanding Google Cloud Platform Pricing | AI/ML Pricing on Google Cloud Platform

Kemal Cholovich — Thu, 25 Jul 2024 17:37:36 +0000

Understanding Google Cloud Platform Pricing

As a GDG Community Leader, I've often been asked about the intricacies of Google Cloud Platform (GCP) pricing. Navigating through the various pricing models can be a bit overwhelming, but with a clear understanding, you can optimize costs effectively. Let's dive into the different pricing points with some examples to make things clearer.

Pricing Overview

GCP pricing is based on the resources you consume, which include compute, storage, and networking. Costs can vary depending on the type of services you use, the region where your resources are located, and how you use them.

Free Trial and Free Tier

Free Trial:
New GCP users receive $300 in free credits to use over 90 days. This allows you to experiment with most GCP services without incurring any costs.

Free Tier:
Certain GCP services offer free usage limits every month. For example:

Compute Engine: 1 f1-micro VM instance per month in specific regions.
Cloud Storage: 5 GB of regional storage.
BigQuery: 1 TB of queries per month.

On Demand

This is the pay-as-you-go model, where you only pay for the resources you use without any long-term commitments.

Example:
If you run an n1-standard-1 VM in the us-central1 region for 10 hours at an hourly rate of $0.0475, the cost would be:

10 hours * $0.0475/hour = $0.475

Committed Use Discounts

By committing to use certain resources for 1 or 3 years, you can save significantly compared to on-demand pricing. This requires an upfront commitment.

Example:
If the on-demand cost for an n1-standard-1 VM is $0.0475 per hour, a 1-year committed use discount might reduce it to $0.033 per hour.

Annual cost: 8760 hours * $0.033/hour = $288.08 (compared to $415.50 on-demand).

Sustained Use Discounts - I love it!

These discounts are automatically applied when you use certain resources consistently for a significant portion of the month. The more you use, the higher the discount.

Example:
If you run a VM for more than 25% of the month, GCP will automatically apply a discount to your usage for the entire month. The discount increases as your usage approaches 100%.

Flat Rate Pricing

For services like BigQuery, you can opt for flat-rate pricing, which offers predictable pricing by charging a fixed monthly fee regardless of actual usage.

Example:
BigQuery's flat-rate pricing option allows you to pay a fixed fee (e.g., $10,000 per month) for a dedicated slot of processing capacity, making it ideal for organizations with high and predictable query loads.

Sole Tenant Node Pricing

This model is ideal for organizations that require dedicated physical servers due to regulatory, compliance, or performance needs. You pay for the exclusive use of a physical server, which can run multiple virtual machines.

Example:
Suppose a sole tenant node costs $1,000 per month. You can run multiple VMs on this node, and the total cost remains $1,000, providing dedicated hardware and isolation for your workloads.

Banking Example with Sole Tenant Nodes

Let's say you're a bank that needs to run sensitive financial applications requiring strict compliance and data isolation. By using sole tenant nodes, you ensure that your data and applications run on dedicated hardware without sharing resources with other customers. This isolation helps meet regulatory requirements and enhances security.

Example:
If your bank runs several critical applications on a sole tenant node costing $2,000 per month, you can ensure these applications are isolated, reducing the risk of data breaches. Additionally, you can run multiple virtual machines on this dedicated node, optimizing the use of the physical hardware while maintaining compliance.

AI/ML Pricing on Google Cloud Platform

Google Cloud Platform (GCP) offers a range of AI and ML services designed to help you build, deploy, and scale your AI solutions. The pricing for these services varies based on usage and the specific service you are using. Let's break down the pricing for some commonly used AI and ML services on GCP with examples.

1. AI Platform Training and Prediction

AI Platform Training allows you to train machine learning models at scale. The cost depends on the type of machine (e.g., standard, high-memory, high-CPU, GPU) and the region where the training is performed.

Example:

If you use a n1-standard-4 VM (4 vCPUs, 15 GB memory) for training in the us-central1 region, and the hourly rate is $0.15, training for 10 hours would cost:
- 10 hours * $0.15/hour = $1.50
If you add a Tesla K80 GPU, which costs $0.45 per hour, the total cost for 10 hours would be:
- 10 hours * ($0.15 + $0.45) = $6.00

AI Platform Prediction allows you to deploy trained models for making predictions. The cost is based on the number of predictions and the type of model deployed.

Example:

Suppose you deploy a model on a n1-standard-2 VM, which costs $0.10 per hour, and you use it for 100 hours:
- 100 hours * $0.10/hour = $10.00
If you make 1 million predictions, and the cost per prediction is $0.0001, the total cost for predictions would be:
- 1,000,000 predictions * $0.0001/prediction = $100.00

2. BigQuery ML

BigQuery ML enables you to create and execute machine learning models directly in BigQuery using SQL.

Example:

If you create a linear regression model on a dataset that has 10 million rows, and it takes 50 GB of processed data, the cost would be based on BigQuery's on-demand pricing.
Assuming the cost is $5 per TB of processed data:
- (50 GB / 1024) * $5 = $0.24

3. AutoML

AutoML allows you to build custom machine learning models with minimal expertise. Pricing is based on the training hours and the prediction requests.

Example:

Training an image classification model for 10 hours on a n1-standard-8 VM with a Tesla P100 GPU, where the VM costs $0.50 per hour and the GPU costs $1.50 per hour:
- 10 hours * ($0.50 + $1.50) = $20.00
Making 1,000 predictions with the trained model at $0.0005 per prediction:
- 1,000 predictions * $0.0005/prediction = $0.50

4. Dialogflow

Dialogflow is used for building conversational interfaces like chatbots. The pricing is based on the number of requests and the edition used (Standard or Enterprise).

Example:

Using Dialogflow Essentials (Standard edition) for 10,000 requests per month, where the first 1,000 requests are free, and each additional request costs $0.002:
- (10,000 - 1,000) * $0.002 = $18.00

5. Cloud Vision API

Cloud Vision API enables image analysis and detection of objects, faces, text, and more. The pricing is based on the number of images processed.

Example:

Processing 1,000 images per month, with the first 1,000 units free, and additional units costing $1.50 per 1,000 units:
- 1,000 images * $0.0015 = $1.50 (if exceeding free tier)

6. Cloud Natural Language API

This service is used for analyzing and extracting insights from text. Pricing is based on the number of text units processed.

Example:

Analyzing 500,000 text units per month, where the first 5,000 units are free, and additional units cost $0.0001 each:
- (500,000 - 5,000) * $0.0001 = $49.50

Let's verify the provided statements about Vertex AI pricing based on the most recent and accurate information available online:

7. Vertex AI

Vertex AI is Google's unified AI platform that helps you build, deploy, and scale ML models. Pricing is based on the training and deployment resources you use.

Example:

Training a model: Suppose you need to train a machine learning model using Vertex AI. You use an n1-standard-8 VM (8 vCPUs, 30 GB memory) and a Tesla T4 GPU. The VM costs $0.379 per hour, and the GPU costs $0.35 per hour.
- Training time: 10 hours
- Cost calculation: 10 hours * ($0.379 + $0.35) = $7.29
Data involved: Let's assume your training dataset is 100 GB. If your data is stored in Google Cloud Storage and you access it during training, you might incur additional storage costs. The cost for standard storage in the multi-region (us) is $0.026 per GB per month.
- Storage cost: 100 GB * $0.026/GB/month = $2.60 per month
Deploying the model for online prediction: You deploy the trained model on a n1-standard-4 VM (4 vCPUs, 15 GB memory) for making online predictions. The VM costs $0.19 per hour.
- Deployment time: 100 hours
- Cost calculation: 100 hours * $0.19/hour = $19.00
Data involved: Suppose you serve predictions for 10,000 data points, each 1 KB in size.
- Data transferred: 10,000 * 1 KB = 10 MB
- If you need to move this data out of the GCP region, additional network egress charges might apply, typically around $0.12 per GB for the first 10 TB.

8. Vertex AI GenAI Models

Vertex AI GenAI Models offer pre-trained generative AI models that you can use directly or fine-tune for your specific needs. Pricing is based on the number of requests and the complexity of the model.

Example:

Using a GenAI text generation model: You utilize a pre-trained text generation model for generating 50,000 text responses. Each request costs $0.002.
- Cost calculation: 50,000 requests * $0.002/request = $100.00
Data involved: Suppose each text response generated is about 2 KB.
- Total data generated: 50,000 requests * 2 KB = 100,000 KB = 100 MB
Fine-tuning the model: If you fine-tune the model with an additional 10 GB of training data, and you use a similar n1-standard-8 VM with a Tesla T4 GPU for 20 hours, the cost would be:
- Fine-tuning cost: 20 hours * ($0.379 + $0.35) = $14.58
- Storage cost for 10 GB: 10 GB * $0.026/GB/month = $0.26 per month

9. Vertex AI Agent Builder

Vertex AI Agent Builder helps you create and manage AI agents (e.g., chatbots). Pricing depends on the number of agents, the complexity of the interactions, and the compute resources used.

Example:

Creating an AI agent: You develop an AI chatbot that handles 20,000 interactions per month. Each interaction costs $0.001.
- Cost calculation: 20,000 interactions * $0.001/interaction = $20.00
Data involved: Suppose each interaction involves 1 KB of data.
- Total data processed: 20,000 interactions * 1 KB = 20 MB
Additional compute resources: If your chatbot needs to handle complex interactions requiring more processing power, you might use a n1-standard-4 VM (4 vCPUs, 15 GB memory) costing $0.19 per hour for an additional 50 hours.
- Additional compute cost: 50 hours * $0.19/hour = $9.50
Data storage: If your chatbot logs interaction data amounting to 5 GB per month, the storage cost would be:
- Storage cost: 5 GB * $0.026/GB/month = $0.13 per month

Important to know!

Vertex AI costs are based on the type of VM and GPU used, along with the duration of use.
Vertex AI GenAI Models costs are calculated per request and additional fine-tuning costs.
Vertex AI Agent Builder costs are based on interactions and additional compute resources needed.

Conclusion

For more detailed calculations and to stay updated on any pricing changes, I highly recommend using the GCP pricing calculator. It's a valuable tool to estimate costs based on your specific usage and requirements, and prices can change frequently, so it's good to check it regularly.

As you explore GCP, understanding these pricing models can help you make informed decisions, optimize costs, and leverage the full potential of Google Cloud's services.

Happy Clouding,
Kemal Cholovich

What is QUALIFY in BigQuery | Understanding QUALIFY in BigQuery: A Guide for Data Scientists | BigQuery Tips

Kemal Cholovich — Wed, 17 Jul 2024 16:12:30 +0000

Understanding `QUALIFY` in BigQuery

As a seasoned Data Scientist, I've often found myself working with large datasets and complex queries. One powerful feature in BigQuery that has streamlined my workflow is the QUALIFY clause. This blog post aims to demystify QUALIFY, demonstrating how it can be used to filter results based on window functions. By the end, you'll see how this clause can simplify and enhance your data querying process.

What is `QUALIFY`?

QUALIFY is a clause in BigQuery used to filter the results of a query based on the output of window functions. It operates similarly to WHERE and HAVING but specifically applies to the results after window functions are computed.

Why Use `QUALIFY`?

In scenarios where you need to filter rows based on the results of window functions, QUALIFY can make your queries more readable and concise. Instead of nesting subqueries or using complex joins, you can directly filter the results of window functions.

Example 1: Finding Top Sales per Employee

Let's start with a simple example. Suppose we have a sales table containing the following columns: sale_id, employee_id, and sale_amount. We want to find the top sale made by each employee.

Sample Data

WITH sales AS (
  SELECT 1 AS sale_id, 101 AS employee_id, 100 AS sale_amount UNION ALL
  SELECT 2, 101, 150 UNION ALL
  SELECT 3, 102, 200 UNION ALL
  SELECT 4, 102, 50 UNION ALL
  SELECT 5, 103, 300
)

Query

Without QUALIFY, you might write:

SELECT *
FROM (
  SELECT
    sale_id,
    employee_id,
    sale_amount,
    ROW_NUMBER() OVER (PARTITION BY employee_id ORDER BY sale_amount DESC) AS row_num
  FROM sales
)
WHERE row_num = 1;

With QUALIFY, the query becomes more concise:

WITH sales AS (
  SELECT 1 AS sale_id, 101 AS employee_id, 100 AS sale_amount UNION ALL
  SELECT 2, 101, 150 UNION ALL
  SELECT 3, 102, 200 UNION ALL
  SELECT 4, 102, 50 UNION ALL
  SELECT 5, 103, 300
)
SELECT
  sale_id,
  employee_id,
  sale_amount,
  ROW_NUMBER() OVER (PARTITION BY employee_id ORDER BY sale_amount DESC) AS row_num
FROM sales
QUALIFY row_num = 1;

Example 2: Ranking Products by Sales

Now, let's consider a products table where we want to rank products based on their sales and filter the top 3 products for each category.

Sample Data

WITH products AS (
  SELECT 1 AS product_id, 'A' AS category, 500 AS sales UNION ALL
  SELECT 2, 'A', 600 UNION ALL
  SELECT 3, 'A', 200 UNION ALL
  SELECT 4, 'A', 700 UNION ALL
  SELECT 5, 'B', 300 UNION ALL
  SELECT 6, 'B', 400 UNION ALL
  SELECT 7, 'B', 100 UNION ALL
  SELECT 8, 'B', 200
)

Query

WITH products AS (
  SELECT 1 AS product_id, 'A' AS category, 500 AS sales UNION ALL
  SELECT 2, 'A', 600 UNION ALL
  SELECT 3, 'A', 200 UNION ALL
  SELECT 4, 'A', 700 UNION ALL
  SELECT 5, 'B', 300 UNION ALL
  SELECT 6, 'B', 400 UNION ALL
  SELECT 7, 'B', 100 UNION ALL
  SELECT 8, 'B', 200
)
SELECT
  product_id,
  category,
  sales,
  RANK() OVER (PARTITION BY category ORDER BY sales DESC) AS sales_rank
FROM products
QUALIFY sales_rank <= 3;

Example 3: Filtering Employees by Tenure and Performance

In an employees table, we want to identify employees who rank in the top 2 by performance score within each department and have been with the company for over 5 years.

Sample Data

WITH employees AS (
  SELECT 1 AS employee_id, 'HR' AS department, 90 AS performance_score, 6 AS years_with_company UNION ALL
  SELECT 2, 'HR', 85, 4 UNION ALL
  SELECT 3, 'HR', 95, 7 UNION ALL
  SELECT 4, 'IT', 88, 10 UNION ALL
  SELECT 5, 'IT', 75, 3 UNION ALL
  SELECT 6, 'IT', 92, 6
)

Query

WITH employees AS (
  SELECT 1 AS employee_id, 'HR' AS department, 90 AS performance_score, 6 AS years_with_company UNION ALL
  SELECT 2, 'HR', 85, 4 UNION ALL
  SELECT 3, 'HR', 95, 7 UNION ALL
  SELECT 4, 'IT', 88, 10 UNION ALL
  SELECT 5, 'IT', 75, 3 UNION ALL
  SELECT 6, 'IT', 92, 6
)
SELECT
  employee_id,
  department,
  performance_score,
  years_with_company,
  RANK() OVER (PARTITION BY department ORDER BY performance_score DESC) AS perf_rank
FROM employees
QUALIFY perf_rank <= 2 AND years_with_company > 5;

Conclusion

The QUALIFY clause in BigQuery is a powerful tool for filtering results based on window functions. It simplifies queries, making them more readable and maintainable. Whether you're ranking sales, identifying top-performing products, or filtering employees based on performance and tenure, QUALIFY can enhance your data querying capabilities.

By incorporating QUALIFY into your BigQuery toolkit, you can write cleaner, more efficient SQL queries, ultimately saving time and improving the accuracy of your data analysis.

Resources for Learning About `QUALIFY` in BigQuery:

Google Cloud BigQuery Documentation
- Overview: The official Google Cloud BigQuery documentation is the most authoritative source for understanding all features of BigQuery, including the QUALIFY clause. It provides detailed explanations, syntax, and examples.
- Why It's Useful: This resource is maintained by Google, ensuring that it is up-to-date with the latest features and best practices. It also includes links to related concepts and advanced usage scenarios.
- Link: BigQuery Documentation - QUALIFY Clause
Stack Overflow
- Overview: Stack Overflow is a community-driven Q&A platform where you can find real-world problems and solutions related to BigQuery and the QUALIFY clause. Experienced data scientists and SQL experts often share their insights and solutions here.
- Why It's Useful: This resource provides a diverse range of examples and solutions to specific issues that other users have encountered. You can also ask your own questions and get responses from the community.
- Link: Stack Overflow - Questions Tagged with BigQuery
DataCamp - BigQuery SQL for Data Analysis
- Overview: DataCamp offers an interactive course specifically focused on SQL for data analysis using BigQuery. This course covers various SQL functions, including the use of window functions and the QUALIFY clause.
- Why It's Useful: DataCamp's hands-on approach allows you to practice writing and executing queries in a simulated BigQuery environment. This practical experience can help solidify your understanding of the QUALIFY clause and other advanced SQL concepts.
- Link: DataCamp - BigQuery SQL for Data Analysis

Best,
Kemal Cholovich

What is file-level restore (FLR)? | Understanding File-Level Restore (FLR): Simple Explanation by Examples | Backup Tips

Kemal Cholovich — Tue, 16 Jul 2024 14:13:44 +0000

File-Level Restore (FLR) is a feature that lets you restore specific files or folders from a backup without having to restore the entire system. This can save a lot of time and make the recovery process much easier.

What is File-Level Restore (FLR)?

FLR allows you to recover just the files you need, like a single document or folder, instead of restoring everything from a backup. It's a bit like having a magic rewind button for your files.

Why is FLR Important?

Precision: Only recover the files you need.
Speed: Quickly get back important files without waiting for a full system restore.
Convenience: Restore files to their original location or somewhere else if needed.
Control: Recover older versions of files that might have been changed or deleted.

Simple Examples of FLR

Example 1: Accidental Deletion

Scenario: You accidentally delete a report you’ve been working on.

Solution: Using FLR, you can go into your backup, find the report, and restore just that file back to your computer.

Example 2: File Corruption

Scenario: A project file gets corrupted and won’t open.

Solution: With FLR, you can find an uncorrupted version of the file from your backup and restore it, so you can continue working without losing time.

Example 3: Ransomware Attack

Scenario: A virus encrypts your files, making them unusable.

Solution: FLR allows you to restore only the files affected by the virus from a backup taken before the attack, so you don't have to restore everything.

Example 4: Recovering Older Versions

Scenario: You save an important document by mistake.

Solution: FLR lets you go back to a previous version of the document from your backup and restore it, so you can recover the information you lost.

HYCU’s Support for File-Level Restore

HYCU, a company that provides backup and recovery solutions, supports File-Level Restore. This means with HYCU, you can easily restore specific files or folders from your backups.

Want to Know More?

To learn more about how HYCU can help with your data recovery needs, visit the HYCU Contact Page and talk to their experts. They can help you set up a backup and recovery system that works for your business.

Best,
Kemal Cholovich

Bulletproof Your Analysis: Data Quality Checklists for Reliable Insights

Kemal Cholovich — Sat, 27 Apr 2024 22:44:02 +0000

How I Measure Data Quality (And How You Can Too)

Hi everyone! I'm a senior data scientist with over ten years in the field, and there's one question that pops up all the time: "How do I know my data quality is good enough?" If you've struggled with that too, this post is for you.

Metrics, Metrics, Metrics

The key is to measure your data on those quality dimensions that really matter for what you're doing. You need numbers – that's the only way to get a reliable picture. So, I highly recommend using data quality KPIs (Key Performance Indicators).

Data Quality: It's All About Purpose

Data is good enough when it works the way it's supposed to. Picture this: You've got a fancy real-time fraud detection system. Speed is everything, right? You need super fresh data. So for you, 'timeliness' is your most important data quality metric.

But what if your focus is keeping customers happy? Now accurate information is crucial. You can't send offers for products that aren't in stock just because your tables are mismatched. In this case, accuracy and validity steal the spotlight.

Introducing Data Quality Dimensions

Here's where things get organized. Think of these as categories of common problems:

Timeliness: Is your data up-to-date?
Validity: Are values in the right format?
Accuracy: Does the data reflect reality?
Completeness: Any missing info?
Uniqueness: Are there annoying duplicates?

KPIs: Your Data Quality Scorecard

Here's how to actually measure data quality:

Set up Checks: What do you need to monitor?
Match to Dimensions: Which dimensions do your checks fall under?
Count the Wins: Track how often your checks pass.
The Percentage Power: That's your KPI – percentage of passed checks (over a day, week, etc.)

Each dimension can have its own KPI. Say you get these results:

Timeliness: 90% (Yikes, missed some opportunities!)
Validity: 95% (Still, 5% of customers getting bad info isn't cool)

Below are the top 10 online references that can provide valuable insights into creating and implementing data quality checklists to ensure reliable analytics. These sources include a mix of articles, industry guidelines, and academic papers that are highly regarded in the field of data science:

Top 10 References on Data Quality Checklists for Reliable Insights:

"Data Quality: The Accuracy Dimension" - Jack E. Olson
- This book provides an in-depth look at data quality with a focus on accuracy, including practical frameworks for assessing and improving data quality.
- Link to the book
"Data Quality Assessment" - Arkady Maydanchik
- Arkady Maydanchik offers methodologies for data quality assessment that are crucial for any organization looking to ensure the reliability of their datasets.
- Link to the book
"Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information™" - Danette McGilvray
- This book outlines a ten-step process for planning and implementing data quality projects, which can be extremely useful for creating a checklist for data quality.
- Link to the book
"The DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK)" - DAMA International
- This comprehensive guide covers various aspects of data management and includes sections on data quality management.
- Link to purchase or access
"Juran's Quality Handbook: The Complete Guide to Performance Excellence, Seventh Edition" - Joseph A. Defeo
- Although not specifically about data quality, this handbook contains essential principles of quality management that can be adapted to data quality initiatives.
- Link to the book
"Measuring Data Quality for Ongoing Improvement: A Data Quality Assessment Framework" - Laura Sebastian-Coleman
- This book introduces a framework for assessing the quality of data, which is crucial in developing effective data quality checklists.
- Link to the book
"Improving Data Warehouse and Business Information Quality: Methods for Reducing Costs and Increasing Profits" - Larry P. English
- Offers insights into improving the quality of data in data warehouses and business intelligence systems.
- Link to the book
"Critical Data Studies: An Introduction to the Critical Role of Data in Society" - Edited by Dalton and Thatcher
- This collection of essays provides critical insights into the implications of data quality in societal contexts.
- Link to more information
"Data Quality for Analytics Using SAS" - Gerhard Svolba
- This book is specific to using SAS for data analytics but provides general principles on ensuring data quality that can be applied broadly.
- Link to the book
"Practical Data Migration" - Johny Morris
- Provides a practical approach to data migration, which includes crucial steps for data quality checking.
- Link to the book

These resources offer a broad perspective on data quality, from theoretical frameworks and methodologies to practical tips and industry-specific guidelines.

Did I Forget Anything?

That's the basics of measuring data quality! Let me know if you have any other tips or favorite techniques.

Let's get those data problems sorted!
Best,
Kemal Cholovich

dataquality #datagovernance #dataengineering #datascience

Unleashing Your Potential: A Python Journey to Expertise and Freedom

Kemal Cholovich — Wed, 24 Apr 2024 20:29:07 +0000

Young engineers, the path of mastering Python is a journey – one that empowers you to shape your future. It's about gaining the skills to command not just code, but also your own destiny.

The resources I'll share have been instrumental in my own 15-year journey as a senior developer, engineer, and data scientist and I believe they can be catalysts for your growth.

Why? Because technical mastery isn't just about the code. True

Python excellence opens doors:

Develop Your Independence:
Build the skills to work effectively from anywhere, with anyone, on projects that ignite your passion.
Unlock Financial Freedom: Command higher compensation, whether as a freelancer, a valued team member, or leading your own venture.
Make a Global Difference: Contribute to groundbreaking projects and collaborate with the world's brightest minds, solving the problems that matter to you.

Learning Python is a lifelong endeavor:

Embrace the challenge, and dedicate yourself to consistent self-improvement. With every line of code you write, every project you build, and every obstacle you overcome, you're not just becoming a better engineer – you're becoming the architect of your own, extraordinary future.

Now, let's dive into the resources that will propel you forward...

🚀 Resource Guide: Propel Your Python Mastery and Remote Work Opportunities

🔍Python Mastery Resources

Dive into these world-class resources to boost your Python skills:

Free Python Courses:
- 100 Days of Python - Free
- Python Course on Scrimba - Free
- Python for Everybody - Free
- Django for Everybody - Free
- Kaggle Python Courses - Free
- Real Python - $49.99/Month
Python Books and IDEs:
- Invent with Python Books - Free Best Python Books
- Learn Python the Hard Way, 5th Edition - Free Best Python Book
- Install Python Guide - Free
- PyCharm IDE - Local Python IDE
- Repl.it Online IDE - Free Online IDE
- Google Colab - Free Online IDE
- W3Schools Python Examples - Free
Python Courses from Top Universities:
- Harvard's CS50 Python Course on YouTube - Free
- MIT's Introduction to CS and Programming, Fall 2022 - Free

🔍Development Environments

Setup your Python development environment with these resources:

How to Install Python - Free
Google Colab Online IDE - Free
Repl.it Online IDE - Free
PyCharm Local IDE - Local Python IDE
VS Code Python Quick Start - Local Python IDE

🔍Learn by Building Projects

Gain real-world experience by engaging with these project-based learning platforms:

Cloud Boost Skills - Free for GDG members, Hands-on Cloud Projects/Labs $300/user
Live Project Learning - $25/Month
Udemy Python Mega Course - 20+ Projects
Educative Real-World Projects - 240+ Projects, $210/Year
Coursera Python Projects - 350+ Projects, €54/Month
KodeKloud Engineer - $70/Month, DevOps Experience

🔍Coding Interview Preparation

Prepare for your coding interviews with these tailored resources:

freeCodeCamp Interview Prep - Free
LeetCode Easy Interview Questions - $35
NeetCode.io - $99/Year
StrataScratch - Coding for Data Science
DataInterview - Coding for Data Science

🔍Remote Work: Your Path to Freedom

Explore the advantages of remote work and find opportunities:

Why Remote? Insights & Resources:
- Rethinking Work: Insights from a Remote Veteran
Remote Job Boards:
- FlexJobs - Hand-screened Remote Jobs
- Remote.co - Insights and Remote Jobs
- We Work Remotely - Largest Remote Job Board
- Remotive - Remote Jobs and Community
- Virtual Vocations - Telecommute Jobs
- Pangian - Travel and Work
- Jobspresso - Curated Remote Jobs
- Toptal - Freelance Talent Network
- Crossover - Full-time Remote Jobs
- Turning.io - Software Projects
- Indeed Remote Jobs
- Remote OK
- Nomad List

🔍Final Thoughts

Self-Direction: Take initiative and stay consistent. These resources are guides to help you along your journey.
Growth Mindset: Mastery in Python is about continual learning and improvement.
Real-World Impact: Use your skills to create solutions that make a difference.

Let me know if there's anything you'd like changed or more emphasis put on certain parts!

Rethinking Work: My Takeaways from "Remote" and "Rework" as a Remote Veteran

Kemal Cholovich — Wed, 24 Apr 2024 18:58:02 +0000

As a senior developer, with over a decade of remote work experience for clients across the globe, I've seen the evolution of remote teams and the changing attitudes towards this way of working. Two books, "Remote" by Jason Fried and David Heinemeier Hansson (the founders of Basecamp) and "Rework" by the same authors, have been particularly insightful companions on my journey. Here's my perspective on these groundbreaking books and why they still hold relevance today.

"Remote": It's Not About Where You Work, But How You Work

One of the core messages of "Remote" is that "office-centrism" is outdated. The authors break down the myths that remote work leads to isolation, lack of productivity, and disengaged teams. Instead, they argue that remote work forces you to build better systems of communication, documentation, and trust.

As someone who's managed remote teams and been a part of them, I resonate deeply with this. When physical proximity isn't a crutch, you're compelled to focus on clear asynchronous communication, effective project management, and results-driven work.

"Rework": Challenging Conventional Business Wisdom

While not explicitly about remote work, "Rework" advocates for a lean, iterative, and customer-centric approach to doing business. Many of its principles translate perfectly to a remote setting. Here are some takeaways that have helped me over the years:

"Start small, not big" – This is essential for remote teams. Begin with focused projects, and clear objectives, and prioritize getting things done over endless planning.
"Embrace constraints" – Remote work has inherent limitations. Use these to your advantage to innovate and be more efficient.
"Ignore the details early on" – Get something working first, then iterate. This is key when team members are across various time zones. Get the core functionality right, then finesse.
"Underdo competitors" – Focus on delivering exceptional value in a specific niche, rather than trying to be everything to everyone.

My Experience: The Good, The Bad, and The Lessons

Over a decade of remote work has taught me both the immense benefits and the undeniable challenges:

Pros:
- Flexibility and control over my schedule
- Greater work-life balance
- Access to a global talent pool
- Reduced commuting stress
Cons:
- Potential for isolation (combat this with virtual gatherings and co-working)
- Need for strong self-discipline
- Occasional misalignment due to time zone differences
- Reliance on technology (have solid backups in place!)

The Pandemic Effect: Remote Work Goes Mainstream

The COVID-19 pandemic forced many companies to adopt remote work virtually overnight. While there were growing pains, this mass experiment undeniably demonstrated that remote teams can be just as, if not more, productive and innovative as traditional office-based setups.

Staying Ahead in the Remote Work World

With remote work becoming the norm, how do seasoned remote workers like myself maintain our edge? Here's my advice:

Master asynchronous communication: Become a pro at clear written communication.
Continuously refine workflows: Optimize your tools and processes for smoother collaboration.
Be a proactive problem-solver: Take initiative and preempt potential roadblocks.
Build strong relationships virtually: Make a conscious effort to connect with teammates on a personal level.

Top 10 Remote Work Resources

Here are my go-to resources for everything remote:

FlexJobs: https://www.flexjobs.com/
Remote.co: https://remote.co/
We Work Remotely: https://weworkremotely.com/
Remotive: https://remotive.com/
Virtual Vocations: https://www.virtualvocations.com/
Pangian: https://pangian.com/
Jobspresso: https://jobspresso.co/
Toptal: https://www.toptal.com/
Crossover: https://www.crossover.com/
Turning.io: https://www.turning.io/

Let me know your thoughts on "Remote" and "Rework", and share your own remote work tips!

Ultimate Guide to AWS S3 Buckets in 2024: New Features, Storage Smarts, and Beyond

Kemal Cholovich — Tue, 23 Apr 2024 14:33:30 +0000

Intro: Buckets and Beyond

Hey engineers, ready to supercharge your cloud storage game? Amazon Web Services (AWS) S3 buckets are the backbone of countless applications, and they keep getting better. In this guide, we'll dive into the latest S3 features, the crucial differences between General Purpose and Directory buckets, and how to optimize your storage in 2024. Let's get started!

What's Hot in AWS S3: New Features for Your Arsenal

AWS is always innovating, and S3 buckets are no exception. Here are a few recent tools to transform your storage strategy:

Storage Class Analysis: Get AI-powered storage recommendations. This tool studies your data usage and suggests the most cost-effective storage classes (like S3 Standard for frequently used data or S3 Glacier Deep Archive for long-term backups). It's like having a personal storage consultant!
S3 Glacier Instant Retrieval: Think of this as the express pass for archived data. Normally, retrieving data from Glacier storage classes can take hours, but Instant Retrieval gives you access within minutes - perfect for those unexpected "I need that old file NOW!" moments.
Object Lambda: Automate tasks directly within your bucket. Object Lambda functions can transform your data on the fly. Need to resize images on upload, create thumbnails, or even extract metadata? Object Lambda has you covered, saving you separate processing steps.

Bucket Showdown: General Purpose vs. Directory

Choosing the right bucket type is fundamental for efficient storage. Let's demystify the two main players:

General Purpose Buckets: Your versatile workhorses. They're perfect for diverse objects like images, documents, application code, backups – you name it! Think of them as your giant, super-organized toolbox for all your data needs.
Directory Buckets: These are your specialists for massive file hierarchies. They mimic a traditional file system with folders and subfolders, making them ideal for complex data organization. Imagine a multinational company using Directory buckets to organize files for every department, project, and regional office.

Key Differences at a Glance

Feature	General Purpose Bucket	Directory Bucket
Use Case	Storing all types of objects	Storing massive file hierarchies
Organization	Flat structure	Hierarchical structure
Access Patterns	Varied access patterns	Primarily sequential reads
Performance	Optimized for diverse access	Optimized for reading directory listings
Cost Considerations	May be less cost-effective for directory-like structures	More cost-effective for directory-like structures

Real-World Scenarios

General Purpose Bucket: A video-on-demand platform could use a General Purpose bucket to store millions of videos, audio files, subtitles, and customer data all in one place.
Directory Bucket: A software development company might use a Directory bucket to organize source code, project documentation, and build artifacts, with clear folders for different teams and project versions.

How to Backup and Restore S3

The best way to protect your S3 data and make it ready for recovery is over HYCU, which supports both kinds of buckets as well. HYCU R-Cloud HYCU R-Cloud for AWS is a cloud-native Data Protection as a Service that was purpose-built on AWS, for AWS, to deliver secure backup with rapid recovery, seamless data mobility, and migration, and cost-effective DR.

Making the Winning Choice

The key lies in understanding your data. Need flexibility with diverse file types and varied access patterns? General Purpose is your go-to. Managing a massive, structured file system? Directory buckets streamline your organization.

Bonus Tip: Experiment! S3 makes it easy to create and test different bucket types. See what works best for your unique workload.

Mastering S3 in 2024 and Beyond

By harnessing the latest S3 features and choosing bucket types wisely, you'll become a cloud storage pro. Optimize your costs, streamline your data workflows, and keep your applications running smoothly. Now that's what I call smart storage!

Top Resources for S3 Mastery

AWS S3 Documentation (https://aws.amazon.com/s3/)
A Cloud Guru’s S3 Course (https://acloudguru.com)
AWS re:Invent S3 Presentations (https://www.youtube.com)
S3 User Forums (https://forums.aws.amazon.com/)
AWS Architecture Blog (https://aws.amazon.com/blogs/architecture/)
AWS Certified Solutions Architect Study Guide Link
"AWS in Action" Series BOOK

Maximizing Morning Productivity for Data Scientists: Unlocking Flow State

Kemal Cholovich — Fri, 05 Apr 2024 16:26:30 +0000

As a data scientist, my mornings are crucial for setting the pace of my day. With a schedule packed with data analysis, model development, and problem-solving, efficiency is not just a goal—it's a necessity. I've experimented with various morning routines, from time-intensive biohacker rituals to immediately diving into work, often finding myself overwhelmed by midday.

My perspective shifted dramatically after discovering insights from Ryan Doris, co-founder of Flow Research Collective. Doris suggests a balanced approach to morning routines, optimizing for what he calls "flow state productivity," a concept that resonates deeply with the demands of data science.

The Magic of Flow State

Flow state, or being "in the zone," represents peak focus and productivity. Doris points out that our brains are naturally inclined towards this state in the morning, presenting a unique opportunity to enhance our work efficiency from the start.

Moving Beyond Extreme Routines

While biohacker routines offer benefits, their extensive time requirements can be impractical. Conversely, skipping morning preparations entirely risks burnout. The solution lies in a balanced routine, focusing on high-priority tasks to leverage the morning's flow state potential.

Strategic Morning Focus for Data Scientists

Implementing Doris's advice, I now dedicate the first part of my morning to my most critical tasks—whether it's advancing a complex model, perfecting a visualization, or solving intricate code problems. This focus ensures substantial progress on essential projects early in the day.

Preparation: The Key to a Smooth Start

Doris emphasizes preparing the night before to eliminate morning indecision. For a data scientist, this could mean setting up datasets, organizing tools, or planning code structure. This foresight allows for immediate, impactful work upon starting the day.

Experiencing the Benefits Firsthand

This structured approach has transformed my mornings from chaotic to productive, allowing me to tackle significant challenges while my mental clarity is at its peak. It not only improves my morning productivity but also sets a positive tone for the entire day.

Conclusion: A Game-Changer for Data Scientists

Adopting this focused, balanced morning routine has markedly improved my productivity and well-being. For my fellow data scientists struggling with morning productivity, integrating these strategies could be your key to unlocking unparalleled efficiency and focus.

Resources:

Enhancing Productivity for Data Scientists:

6 Productivity Tips for Beginner Data Scientists - Towards AI offers essential advice, including the importance of building projects and starting to write to solidify understanding and showcase skills. Read More
5 Productivity Tips for Data Scientists - Winder.ai discusses anticipating data needs, developing tooling for automation, and improving software engineering and MLOps skills. Read More
10 Jupyter Notebook Tips and Tricks for Data Scientists - KDnuggets provides practical tips for using Jupyter Notebooks more effectively. Read More
Data Science Productivity Tools You Need to Have in 2023 - Oslash highlights essential tools for project management, package management, visualization, and scalable cloud computing. Read More
Tips for Building a Successful Data Science Workflow - Built In offers insights into creating reproducible and stable workflows with cross-functional collaboration. Read More
Increase Productivity in Your Data Science Team - IBM Nordic Blog discusses challenges and strategies for data science teams, emphasizing collaboration and leveraging open-source tools. Read More
10 AI Productivity Tools For Data Scientist In 2023 - AI-pow lists AI productivity tools like DataCamp Workspace AI, Scikit Learn, AutoML, and skills.ai. Read More
Top 10 AI-Powered Tools To Enhance Productivity For Data Scientists - TechModena explores AI-powered tools for predictive modeling, data visualization, and more. Read More

Ryan Doris and Flow State:

Rian Doris' Official Website - Offers insights into Rian's work and the mission of the Flow Research Collective. Visit Site
Flow Research Collective - About Rian Doris - Provides a detailed biography of Rian Doris and the Collective's focus areas. Learn More
Flow Research Collective - Leading Voice In Performance - Describes the organization's aim and training offerings. Explore
Flow Research Collective Radio on Apple Podcasts - Features discussions on peak performance and flow with leading experts. Listen on Apple Podcasts

These resources offer valuable insights and practical advice for both enhancing data science productivity and understanding the principles of flow state for peak performance.

Best,
Kemal Cholovich

Building Machine Learning Projects: Tips for Developers to Learn Effectively

Kemal Cholovich — Wed, 03 Apr 2024 06:53:41 +0000

As a data scientist, I'm always seeking to deepen my understanding of machine learning. It's a fascinating, complex field, and those "aha!" moments when complex concepts click are incredibly rewarding. Along the way, I've picked up a few strategies that have been total game-changers in my learning process. Let's dive into my top 5:

1. It's About Understanding, Not Memorizing

Ever find yourself staring at a formula, feeling like it's written in hieroglyphics? I've been there! The temptation is to memorize, but the key to true comprehension is understanding. Focus on the big-picture idea behind the math – what the formula is actually trying to achieve. Once the concept clicks, the formula naturally makes more sense.

2. Break Down Derivations Like a Puzzle

Derivations can feel overwhelming, but there's a trick! Think of them as multi-step puzzles. Break down those complex derivations into smaller, bite-sized pieces. Approaching it step-by-step makes understanding the process, and therefore replicating it, much easier.

3. Debugging: Your Coding Superpower

Let's be real, code rarely works perfectly the first time. Debugging is where the magic happens! Instead of getting frustrated, embrace debugging as a way to level up your coding skills. With each bug you squash, you'll get better at understanding your code and preventing similar issues in the future.

4. Conquer Large Codebases One Bite at a Time

Diving headfirst into a giant codebase can be daunting. My advice? Start small! Focus on specific, manageable projects within the larger codebase. This way, you'll build your understanding incrementally, turning a mountain into a series of molehills.

5. Persistence is Key!

Machine learning is a journey, not a sprint. There will be times when things feel overwhelming. Don't give up! Set realistic expectations, celebrate small wins, and keep plugging away. With time and consistent effort, those complex concepts will start to click into place.

The Takeaway

These aren't just theoretical tips – they're strategies I actively use in my own machine learning journey. If you're looking to accelerate your learning, give them a try! You may be surprised at how much they can streamline the process.

Resources

Here is the list of resources related to the topic:

3Blue1Brown (YouTube Channel): Delivers intuitive and visual explanations of complex mathematical and machine learning concepts.
"The Elements of Statistical Learning" by Hastie, Tibshirani, Friedman: A classic textbook providing a comprehensive overview of statistical methods fundamental to machine learning.
"Mathematics for Machine Learning" by Marc Deisenroth, A. Aldo Faisal, and Cheng Soon Ong: A fantastic reference that covers the essential math required for a deeper understanding of machine learning algorithms.
MIT OpenCourseWare: Linear Algebra: Courses providing a solid foundation for derivations.
Stack Overflow: The go-to Q&A platform for troubleshooting common and unique programming problems.
Real Python: Debugging in Python: Guides and tutorials to help squash those pesky bugs.
Project-Based Learning Communities: Platforms like Kaggle offer real-world project examples to help you learn by doing.
Coursera - Machine Learning by Andrew Ng: One of the most popular introductions to machine learning.
Machine Learning Bookcamp
Let me know in the comments if you have any of your own machine learning learning secrets to share!

Alternatives to Git in 2024: What Every Developer Must Know

Kemal Cholovich — Tue, 26 Mar 2024 18:50:31 +0000

While Git remains a popular powerhouse in version control, recognizing situations where alternatives might excel is crucial. Let's dive into reasons to consider broadening your version control horizons in 2024:

Challenges with Git

Steep Learning Curve: Git's extensive commands can be overwhelming for beginners or less technically inclined team members.
Scaling Issues: Extremely large projects (like Facebook's monorepo) can strain Git's performance limits.
Code-centric Focus: Git shines with code, but projects heavily involving large datasets or non-textual assets may benefit from specialized solutions.

Promising Alternatives

Mercurial (Hg): Prioritizes user-friendliness with a streamlined interface and workflow. Well-suited to smaller teams or projects seeking simplicity.
Fossil: A unique system integrating version control, wiki, bug tracking, and forums. Perfect for projects where heavy documentation and collaboration are key.
Pijul: Based on patch theory, Pijul aims to improve upon Git's merge handling and historical tracking. Its potential is compelling, though adoption remains less widespread than established options.
Subversion (SVN): A classic centralized VCS favoring a linear workflow. Can be ideal when strict control and less focus on complex branching are desired.

The Facebook Example

Facebook's decision to leave Git sheds light on challenges it can face in certain scenarios:

Scaling Limitations: Their massive monorepo pushed Git's limits, hindering developer productivity.
Workflow Needs: Facebook sought stronger centralized control and custom workflows, which Git's decentralized nature doesn't easily support.

Facebook's Solution: Initially, they migrated to Mercurial for its better handling of large repositories. Eventually, they created their own system, "Sapling", tailored to their specific needs.

Key Takeaways

Extreme Scale: Facebook demonstrates Git's potential struggles at extraordinary scales uncommon for most development teams.
Specific Requirements: Their unique workflow heavily influenced their decision.
Context is Key: The "best" version control depends heavily on your project's requirements.

Git vs. Mercurial: Common Differences

Feature	Git	Mercurial
Philosophy	Flexibility, powerful	Simplicity, ease-of-use
Command Structure	Complex, more granular control	Simpler, more intuitive
Branching	Lightweight, easy to create	Branches are heavier, intentional decision
History	Stores changes as snapshots	Stores changes as changesets

Choosing in 2024: Factors to Consider

Team Dynamics: Favor simplicity? Mercurial offers an easier onboarding process.
Project Nature: Do you often work with large datasets or non-text assets?
Collaboration Style: Complex merges a pain point? Pijul's theory could be interesting.
Workflow Needs: Does a centralized model like Subversion better align with your team's structure?

The Takeaway

Git is a strong standard, yet understanding the value of alternatives is empowering. Explore the landscape to find the system that best facilitates your team's success!

References

Mercurial SCM: https://www.mercurial-scm.org/
Fossil: https://www.fossil-scm.org/
Pijul: https://pijul.org/
Apache Subversion: https://subversion.apache.org/
Git SCM: https://git-scm.com/
A Detailed Comparison of Git and Mercurial: URL
Facebook Engineering: Sapling Source Control URL

Best,
Kemal Cholovich

October is Cybersecurity Awareness Month: What and Why

Kemal Cholovich — Thu, 26 Oct 2023 09:11:19 +0000

Cybersecurity Awareness Month (CSAM) is an annual campaign held in October to raise awareness about cybersecurity and encourage people to take steps to protect themselves online. CSAM is led by the Cybersecurity and Infrastructure Security Agency (CISA) and the National Cybersecurity Alliance (NCSA), in collaboration with a broad range of partners from government, industry, and the non-profit sector.

CSAM is important because everyone is at risk of cyberattacks. Cybercriminals use a variety of tactics to steal personal information, financial data, and other sensitive information. They can also damage computer systems and networks, disrupt critical infrastructure, and commit other crimes.

The theme for CSAM 2023 is "See Yourself in Cyber." This theme is meant to remind people that cybersecurity is everyone's responsibility. It is also meant to encourage people to learn more about cybersecurity threats and how to protect themselves.

Key messages for CSAM 2023:

Do Your Part. #BeCyberSmart. This message reminds people that everyone can play a role in protecting themselves and their organizations from cyberattacks.
Passphrases are Stronger Than Passwords. This message encourages people to use strong passphrases instead of passwords. Passphrases are longer and more complex than passwords, which makes them more difficult to crack.
Use Multi-Factor Authentication (MFA). This message encourages people to enable MFA whenever possible. MFA adds an extra layer of security to your accounts by requiring you to enter a code from your phone in addition to your password.
Update Your Software. This message encourages people to install software updates as soon as they are available. Software updates often include security patches that can help protect you from known vulnerabilities.
Beware of Phishing Emails. This message reminds people to be careful about what links they click on and what attachments they open. Phishing emails are one of the most common ways that cybercriminals gain access to people's accounts and devices.

Cybersecurity Awareness is Important for Everyone

Cybersecurity awareness is important for everyone, regardless of their age, technical expertise, or role in society. Cybercriminals do not care who they target. They are only interested in stealing your information or disrupting your life.

Here are some tips for staying safe online:

Be careful about what information you share online. Avoid sharing personal information on social media or other public websites. Only share sensitive information with trusted sources.
Use strong passwords and enable two-factor authentication (2FA) whenever possible.
Keep your software up to date.
Be careful about what links you click on and what attachments you open.
Be skeptical of unsolicited emails and phone calls.

How Organizations Can Promote Cybersecurity Awareness

Organizations can play a vital role in promoting cybersecurity awareness.

Here are some things that organizations can do:

Provide cybersecurity training to employees.
Implement cybersecurity policies and procedures.
Create a culture of security where everyone feels comfortable reporting suspicious activity.
Promote cybersecurity awareness through internal communications and events.
Participate in Cybersecurity Awareness Month and other cybersecurity awareness campaigns.

Cybersecurity awareness is essential for protecting yourself and your organization from cyberattacks. By taking the time to learn about cybersecurity threats and how to protect yourself, you can help keep your information safe and secure.

DEV Community: Kemal Cholovich

Razvoj Karijere kroz Google Developer Grupu: Moje Iskustvo kao Organizatora GDG Sarajevo

Put Učenja i Profesionalna Certifikacija

Malim koracima naprijed:

Karijera kroz GDG Sarajevo Zajednicu

Mogući Ishodi Karijere

Understanding Google Cloud Platform Pricing | AI/ML Pricing on Google Cloud Platform

Understanding Google Cloud Platform Pricing

Pricing Overview

Free Trial and Free Tier

On Demand

Committed Use Discounts

Sustained Use Discounts - I love it!

Flat Rate Pricing

Sole Tenant Node Pricing

Banking Example with Sole Tenant Nodes

AI/ML Pricing on Google Cloud Platform

1. AI Platform Training and Prediction

2. BigQuery ML

3. AutoML

4. Dialogflow

5. Cloud Vision API

6. Cloud Natural Language API

7. Vertex AI

8. Vertex AI GenAI Models

9. Vertex AI Agent Builder

Important to know!

Conclusion

What is QUALIFY in BigQuery | Understanding QUALIFY in BigQuery: A Guide for Data Scientists | BigQuery Tips

Understanding QUALIFY in BigQuery

What is QUALIFY?

Why Use QUALIFY?

Example 1: Finding Top Sales per Employee

Sample Data

Query

Example 2: Ranking Products by Sales

Sample Data

Query

Example 3: Filtering Employees by Tenure and Performance

Sample Data

Query

Conclusion

Resources for Learning About QUALIFY in BigQuery:

What is file-level restore (FLR)? | Understanding File-Level Restore (FLR): Simple Explanation by Examples | Backup Tips

What is File-Level Restore (FLR)?

Why is FLR Important?

Simple Examples of FLR

Example 1: Accidental Deletion

Example 2: File Corruption

Example 3: Ransomware Attack

Example 4: Recovering Older Versions

HYCU’s Support for File-Level Restore

Want to Know More?

Bulletproof Your Analysis: Data Quality Checklists for Reliable Insights

Top 10 References on Data Quality Checklists for Reliable Insights:

dataquality #datagovernance #dataengineering #datascience

Unleashing Your Potential: A Python Journey to Expertise and Freedom

Python excellence opens doors:

🚀 Resource Guide: Propel Your Python Mastery and Remote Work Opportunities

🔍Python Mastery Resources

🔍Development Environments

🔍Learn by Building Projects

🔍Coding Interview Preparation

🔍Remote Work: Your Path to Freedom

🔍Final Thoughts

Rethinking Work: My Takeaways from "Remote" and "Rework" as a Remote Veteran

Ultimate Guide to AWS S3 Buckets in 2024: New Features, Storage Smarts, and Beyond

Maximizing Morning Productivity for Data Scientists: Unlocking Flow State

The Magic of Flow State

Moving Beyond Extreme Routines

Strategic Morning Focus for Data Scientists

Preparation: The Key to a Smooth Start

Experiencing the Benefits Firsthand

Conclusion: A Game-Changer for Data Scientists

Resources:

Enhancing Productivity for Data Scientists:

Ryan Doris and Flow State:

Building Machine Learning Projects: Tips for Developers to Learn Effectively

1. It's About Understanding, Not Memorizing

2. Break Down Derivations Like a Puzzle

Understanding `QUALIFY` in BigQuery

What is `QUALIFY`?

Why Use `QUALIFY`?

Resources for Learning About `QUALIFY` in BigQuery: