#pyspark ml

[mysticpandakid]

What is PySpark? A Beginner’s Guide 

Introduction 

The digital era gives rise to continuous expansion in data production activities. Organizations and businesses need processing systems with enhanced capabilities to process large data amounts efficiently. Large datasets receive poor scalability together with slow processing speed and limited adaptability from conventional data processing tools. PySpark functions as the data processing solution that brings transformation to operations.  

The Python Application Programming Interface called PySpark serves as the distributed computing framework of Apache Spark for fast processing of large data volumes. The platform offers a pleasant interface for users to operate analytics on big data together with real-time search and machine learning operations. Data engineering professionals along with analysts and scientists prefer PySpark because the platform combines Python's flexibility with Apache Spark's processing functions.  

The guide introduces the essential aspects of PySpark while discussing its fundamental elements as well as explaining operational guidelines and hands-on usage. The article illustrates the operation of PySpark through concrete examples and predicted outputs to help viewers understand its functionality better. 

What is PySpark? 

PySpark is an interface that allows users to work with Apache Spark using Python. Apache Spark is a distributed computing framework that processes large datasets in parallel across multiple machines, making it extremely efficient for handling big data. PySpark enables users to leverage Spark’s capabilities while using Python’s simple and intuitive syntax. 

There are several reasons why PySpark is widely used in the industry. First, it is highly scalable, meaning it can handle massive amounts of data efficiently by distributing the workload across multiple nodes in a cluster. Second, it is incredibly fast, as it performs in-memory computation, making it significantly faster than traditional Hadoop-based systems. Third, PySpark supports Python libraries such as Pandas, NumPy, and Scikit-learn, making it an excellent choice for machine learning and data analysis. Additionally, it is flexible, as it can run on Hadoop, Kubernetes, cloud platforms, or even as a standalone cluster. 

Core Components of PySpark 

PySpark consists of several core components that provide different functionalities for working with big data: 

RDD (Resilient Distributed Dataset) – The fundamental unit of PySpark that enables distributed data processing. It is fault-tolerant and can be partitioned across multiple nodes for parallel execution. 

DataFrame API – A more optimized and user-friendly way to work with structured data, similar to Pandas DataFrames. 

Spark SQL – Allows users to query structured data using SQL syntax, making data analysis more intuitive. 

Spark MLlib – A machine learning library that provides various ML algorithms for large-scale data processing. 

Spark Streaming – Enables real-time data processing from sources like Kafka, Flume, and socket streams. 

How PySpark Works 

1. Creating a Spark Session 

To interact with Spark, you need to start a Spark session. 

Output: 

2. Loading Data in PySpark 

PySpark can read data from multiple formats, such as CSV, JSON, and Parquet. 

Expected Output (Sample Data from CSV): 

3. Performing Transformations 

PySpark supports various transformations, such as filtering, grouping, and aggregating data. Here’s an example of filtering data based on a condition. 

Output: 

4. Running SQL Queries in PySpark 

PySpark provides Spark SQL, which allows you to run SQL-like queries on DataFrames. 

Output: 

5. Creating a DataFrame Manually 

You can also create a PySpark DataFrame manually using Python lists. 

Output: 

Use Cases of PySpark 

PySpark is widely used in various domains due to its scalability and speed. Some of the most common applications include: 

Big Data Analytics – Used in finance, healthcare, and e-commerce for analyzing massive datasets. 

ETL Pipelines – Cleans and processes raw data before storing it in a data warehouse. 

Machine Learning at Scale – Uses MLlib for training and deploying machine learning models on large datasets. 

Real-Time Data Processing – Used in log monitoring, fraud detection, and predictive analytics. 

Recommendation Systems – Helps platforms like Netflix and Amazon offer personalized recommendations to users. 

Advantages of PySpark 

There are several reasons why PySpark is a preferred tool for big data processing. First, it is easy to learn, as it uses Python’s simple and intuitive syntax. Second, it processes data faster due to its in-memory computation. Third, PySpark is fault-tolerant, meaning it can automatically recover from failures. Lastly, it is interoperable and can work with multiple big data platforms, cloud services, and databases. 

Getting Started with PySpark 

Installing PySpark 

You can install PySpark using pip with the following command: 

To use PySpark in a Jupyter Notebook, install Jupyter as well: 

To start PySpark in a Jupyter Notebook, create a Spark session: 

Conclusion 

PySpark is an incredibly powerful tool for handling big data analytics, machine learning, and real-time processing. It offers scalability, speed, and flexibility, making it a top choice for data engineers and data scientists. Whether you're working with structured data, large-scale machine learning models, or real-time data streams, PySpark provides an efficient solution. 

With its integration with Python libraries and support for distributed computing, PySpark is widely used in modern big data applications. If you’re looking to process massive datasets efficiently, learning PySpark is a great step forward. 

[azuredata]

Best Azure Data Engineer Course In Ameerpet | Azure Data

Understanding Delta Lake in Databricks

Introduction

Delta Lake, an open-source storage layer developed by Databricks, is designed to address these challenges. It enhances Apache Spark's capabilities by providing ACID transactions, schema enforcement, and time travel, making data lakes more reliable and efficient. In modern data engineering, managing large volumes of data efficiently while ensuring reliability and performance is a key challenge.

What is Delta Lake?

Delta Lake is an optimized storage layer built on Apache Parquet that brings the reliability of a data warehouse to big data processing. It eliminates the limitations of traditional data lakes by adding ACID transactions, scalable metadata handling, and schema evolution. Delta Lake integrates seamlessly with Azure Databricks, Apache Spark, and other cloud-based data solutions, making it a preferred choice for modern data engineering pipelines. Microsoft Azure Data Engineer

Key Features of Delta Lake

1. ACID Transactions

One of the biggest challenges in traditional data lakes is data inconsistency due to concurrent read/write operations. Delta Lake supports ACID (Atomicity, Consistency, Isolation, Durability) transactions, ensuring reliable data updates without corruption. It uses Optimistic Concurrency Control (OCC) to handle multiple transactions simultaneously.

2. Schema Evolution and Enforcement

Delta Lake enforces schema validation to prevent accidental data corruption. If a schema mismatch occurs, Delta Lake will reject the data, ensuring consistency. Additionally, it supports schema evolution, allowing modifications without affecting existing data.

3. Time Travel and Data Versioning

Delta Lake maintains historical versions of data using log-based versioning. This allows users to perform time travel queries, enabling them to revert to previous states of data. This is particularly useful for auditing, rollback, and debugging purposes. Azure Data Engineer Course

4. Scalable Metadata Handling

Traditional data lakes struggle with metadata scalability, especially when handling billions of files. Delta Lake optimizes metadata storage and retrieval, making queries faster and more efficient.

5. Performance Optimizations (Data Skipping and Caching)

Delta Lake improves query performance through data skipping and caching mechanisms. Data skipping allows queries to read only relevant data instead of scanning the entire dataset, reducing processing time. Caching improves speed by storing frequently accessed data in memory.

6. Unified Batch and Streaming Processing

Delta Lake enables seamless integration of batch and real-time streaming workloads. Structured Streaming in Spark can write and read from Delta tables in real-time, ensuring low-latency updates and enabling use cases such as fraud detection and log analytics.

How Delta Lake Works in Databricks?

Delta Lake is tightly integrated with Azure Databricks and Apache Spark, making it easy to use within data pipelines. Below is a basic workflow of how Delta Lake operates: Azure Data Engineering Certification

Data Ingestion: Data is ingested into Delta tables from multiple sources (Kafka, Event Hubs, Blob Storage, etc.).

Data Processing: Spark SQL and PySpark process the data, applying transformations and aggregations.

Data Storage: Processed data is stored in Delta format with ACID compliance.

Query and Analysis: Users can query Delta tables using SQL or Spark.

Version Control & Time Travel: Previous data versions are accessible for rollback and auditing.

Use Cases of Delta Lake

ETL Pipelines: Ensures data reliability with schema validation and ACID transactions.

Machine Learning: Maintains clean and structured historical data for training ML models. Azure Data Engineer Training

Real-time Analytics: Supports streaming data processing for real-time insights.

Data Governance & Compliance: Enables auditing and rollback for regulatory requirements.

Conclusion

Delta Lake in Databricks bridges the gap between traditional data lakes and modern data warehousing solutions by providing reliability, scalability, and performance improvements. With ACID transactions, schema enforcement, time travel, and optimized query performance, Delta Lake is a powerful tool for building efficient and resilient data pipelines. Its seamless integration with Azure Databricks and Apache Spark makes it a preferred choice for data engineers aiming to create high-performance and scalable data architectures.

Trending Courses: Artificial Intelligence, Azure AI Engineer, Informatica Cloud IICS/IDMC (CAI, CDI),

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[aitoolswhitehattoolbox]

Data Engineer - Pyspark,Scala,GCP,Python

Job Description:Loc: Trivandrum,Kochi,Bangalore,Chennai,PuneExp : 5-7 YearsNotice Period : Immediate – 30 daysJob DescriptionUST Global® is seeking a highly skilled Data Engineer with expertise in Python – GCP, Scala and Data Science/ML to join our team. This role focuses on developing, testing, and deploying machine learning models, data analytics solutions, and leveraging cutting-edge…

[antongordon]

Optimizing GPU Costs for Machine Learning on AWS: Anton R Gordon’s Best Practices

As machine learning (ML) models grow in complexity, GPU acceleration has become essential for training deep learning models efficiently. However, high-performance GPUs come at a cost, and without proper optimization, expenses can quickly spiral out of control.

Anton R Gordon, an AI Architect and Cloud Specialist, has developed a strategic framework to optimize GPU costs on AWS while maintaining model performance. In this article, we explore his best practices for reducing GPU expenses without compromising efficiency.

Understanding GPU Costs in Machine Learning

GPUs play a crucial role in training large-scale ML models, particularly for deep learning frameworks like TensorFlow, PyTorch, and JAX. However, on-demand GPU instances on AWS can be expensive, especially when running multiple training jobs over extended periods.

Factors Affecting GPU Costs on AWS

Instance Type Selection – Choosing the wrong GPU instance can lead to wasted resources.

Idle GPU Utilization – Paying for GPUs that remain idle results in unnecessary costs.

Storage and Data Transfer – Storing large datasets inefficiently can add hidden expenses.

Inefficient Hyperparameter Tuning – Running suboptimal experiments increases compute time.

Long Training Cycles – Extended training times lead to higher cloud bills.

To optimize GPU spending, Anton R Gordon recommends strategic cost-cutting techniques that still allow teams to leverage AWS’s powerful infrastructure for ML workloads.

Anton R Gordon’s Best Practices for Reducing GPU Costs

1. Selecting the Right GPU Instance Types

AWS offers multiple GPU instances tailored for different workloads. Anton emphasizes the importance of choosing the right instance type based on model complexity and compute needs.

✔ Best Practice:

Use Amazon EC2 P4/P5 Instances for training large-scale deep learning models.

Leverage G5 instances for inference workloads, as they provide a balance of performance and cost.

Opt for Inferentia-based Inf1 instances for low-cost deep learning inference at scale.

“Not all GPU instances are created equal. Choosing the right type ensures cost efficiency without sacrificing performance.” – Anton R Gordon.

2. Leveraging AWS Spot Instances for Non-Critical Workloads

AWS Spot Instances offer up to 90% cost savings compared to On-Demand instances. Anton recommends using them for non-urgent ML training jobs.

✔ Best Practice:

Run batch training jobs on Spot Instances using Amazon SageMaker Managed Spot Training.

Implement checkpointing mechanisms to avoid losing progress if Spot capacity is interrupted.

Use Amazon EC2 Auto Scaling to automatically manage GPU availability.

3. Using Mixed Precision Training for Faster Model Convergence

Mixed precision training, which combines FP16 (half-precision) and FP32 (full-precision) computation, accelerates training while reducing GPU memory usage.

✔ Best Practice:

Enable Automatic Mixed Precision (AMP) in TensorFlow, PyTorch, or MXNet.

Reduce memory consumption, allowing larger batch sizes for improved training efficiency.

Lower compute time, leading to faster convergence and reduced GPU costs.

“With mixed precision training, models can train faster and at a fraction of the cost.” – Anton R Gordon.

4. Optimizing Data Pipelines for Efficient GPU Utilization

Poor data loading pipelines can create bottlenecks, causing GPUs to sit idle while waiting for data. Anton emphasizes the need for optimized data pipelines.

✔ Best Practice:

Use Amazon FSx for Lustre to accelerate data access.

Preprocess and cache datasets using Amazon S3 and AWS Data Wrangler.

Implement data parallelism with Dask or PySpark for distributed training.

5. Implementing Auto-Scaling for GPU Workloads

To avoid over-provisioning GPU resources, Anton suggests auto-scaling GPU instances to match workload demands.

✔ Best Practice:

Use AWS Auto Scaling to add or remove GPU instances based on real-time demand.

Utilize SageMaker Multi-Model Endpoint (MME) to run multiple models on fewer GPUs.

Implement Lambda + SageMaker hybrid architectures to use GPUs only when needed.

6. Automating Hyperparameter Tuning with SageMaker

Inefficient hyperparameter tuning leads to excessive GPU usage. Anton recommends automated tuning techniques to optimize model performance with minimal compute overhead.

✔ Best Practice:

Use Amazon SageMaker Hyperparameter Optimization (HPO) to automatically find the best configurations.

Leverage Bayesian optimization and reinforcement learning to minimize trial-and-error runs.

Implement early stopping to halt training when improvement plateaus.

“Automating hyperparameter tuning helps avoid costly brute-force searches for the best model configuration.” – Anton R Gordon.

7. Deploying Models Efficiently with AWS Inferentia

Inference workloads can become cost-prohibitive if GPU instances are used inefficiently. Anton recommends offloading inference to AWS Inferential (Inf1) instances for better price performance.

✔ Best Practice:

Deploy optimized TensorFlow and PyTorch models on AWS Inferentia.

Reduce inference latency while lowering costs by up to 50% compared to GPU-based inference.

Use Amazon SageMaker Neo to optimize models for Inferentia-based inference.

Case Study: Reducing GPU Costs by 60% for an AI Startup

Anton R Gordon successfully implemented these cost-cutting techniques for an AI-driven computer vision startup. The company initially used On-Demand GPU instances for training, leading to unsustainable cloud expenses.

✔ Optimization Strategy:

Switched from On-Demand P3 instances to Spot P4 instances for training.

Enabled mixed precision training, reducing training time by 40%.

Moved inference workloads to AWS Inferentia, cutting costs by 50%.

✔ Results:

60% reduction in overall GPU costs.

Faster model training and deployment with improved scalability.

Increased cost efficiency without sacrificing model accuracy.

Conclusion

GPU optimization is critical for any ML team operating at scale. By following Anton R Gordon’s best practices, organizations can:

✅ Select cost-effective GPU instances for training and inference.

✅ Use Spot Instances to reduce GPU expenses by up to 90%.

✅ Implement mixed precision training for faster model convergence.

✅ Optimize data pipelines and hyperparameter tuning for efficient compute usage.

✅ Deploy models efficiently using AWS Inferentia for cost savings.

“Optimizing GPU costs isn’t just about saving money—it’s about building scalable, efficient ML workflows that deliver business value.” – Anton R Gordon.

By implementing these strategies, companies can reduce their cloud bills, enhance ML efficiency, and maximize ROI on AWS infrastructure.

[intelliontechnologies]

How to Integrate Hadoop with Machine Learning & AI

Introduction

With the explosion of big data, businesses are leveraging Machine Learning (ML) and Artificial Intelligence (AI) to gain insights and improve decision-making. However, handling massive datasets efficiently requires a scalable storage and processing solution—this is where Apache Hadoop comes in. By integrating Hadoop with ML and AI, organizations can build powerful data-driven applications. This blog explores how Hadoop enables ML and AI workflows and the best practices for seamless integration.

1. Understanding Hadoop’s Role in Big Data Processing

Hadoop is an open-source framework designed to store and process large-scale datasets across distributed clusters. It consists of:

HDFS (Hadoop Distributed File System): A scalable storage system for big data.

MapReduce: A parallel computing model for processing large datasets.

YARN (Yet Another Resource Negotiator): Manages computing resources across clusters.

Apache Hive, HBase, and Pig: Tools for data querying and management.

Why Use Hadoop for ML & AI?

Scalability: Handles petabytes of data across multiple nodes.

Fault Tolerance: Ensures data availability even in case of failures.

Cost-Effectiveness: Open-source and works on commodity hardware.

Parallel Processing: Speeds up model training and data processing.

2. Integrating Hadoop with Machine Learning & AI

To build AI/ML applications on Hadoop, various integration techniques and tools can be used:

(a) Using Apache Mahout

Apache Mahout is an ML library that runs on top of Hadoop.

It supports classification, clustering, and recommendation algorithms.

Works with MapReduce and Apache Spark for distributed computing.

(b) Hadoop and Apache Spark for ML

Apache Spark’s MLlib is a powerful machine learning library that integrates with Hadoop.

Spark processes data 100x faster than MapReduce, making it ideal for ML workloads.

Supports supervised & unsupervised learning, deep learning, and NLP applications.

(c) Hadoop with TensorFlow & Deep Learning

Hadoop can store large-scale training datasets for TensorFlow and PyTorch.

HDFS and Apache Kafka help in feeding data to deep learning models.

Can be used for image recognition, speech processing, and predictive analytics.

(d) Hadoop with Python and Scikit-Learn

PySpark (Spark’s Python API) enables ML model training on Hadoop clusters.

Scikit-Learn, TensorFlow, and Keras can fetch data directly from HDFS.

Useful for real-time ML applications such as fraud detection and customer segmentation.

3. Steps to Implement Machine Learning on Hadoop

Step 1: Data Collection and Storage

Store large datasets in HDFS or Apache HBase.

Use Apache Flume or Kafka for streaming real-time data.

Step 2: Data Preprocessing

Use Apache Pig or Spark SQL to clean and transform raw data.

Convert unstructured data into a structured format for ML models.

Step 3: Model Training

Choose an ML framework: Mahout, MLlib, or TensorFlow.

Train models using distributed computing with Spark MLlib or MapReduce.

Optimize hyperparameters and improve accuracy using parallel processing.

Step 4: Model Deployment and Predictions

Deploy trained models on Hadoop clusters or cloud-based platforms.

Use Apache Kafka and HDFS to feed real-time data for predictions.

Automate ML workflows using Oozie and Airflow.

4. Real-World Applications of Hadoop & AI Integration

1. Predictive Analytics in Finance

Banks use Hadoop-powered ML models to detect fraud and analyze risk.

Credit scoring and loan approval use HDFS-stored financial data.

2. Healthcare and Medical Research

AI-driven diagnostics process millions of medical records stored in Hadoop.

Drug discovery models train on massive biomedical datasets.

3. E-Commerce and Recommendation Systems

Hadoop enables large-scale customer behavior analysis.

AI models generate real-time product recommendations using Spark MLlib.

4. Cybersecurity and Threat Detection

Hadoop stores network logs and threat intelligence data.

AI models detect anomalies and prevent cyber attacks.

5. Smart Cities and IoT

Hadoop stores IoT sensor data from traffic systems, energy grids, and weather sensors.

AI models analyze patterns for predictive maintenance and smart automation.

5. Best Practices for Hadoop & AI Integration

Use Apache Spark: For faster ML model training instead of MapReduce.

Optimize Storage: Store processed data in Parquet or ORC formats for efficiency.

Enable GPU Acceleration: Use TensorFlow with GPU-enabled Hadoop clusters for deep learning.

Monitor Performance: Use Apache Ambari or Cloudera Manager for cluster performance monitoring.

Security & Compliance: Implement Kerberos authentication and encryption to secure sensitive data.

Conclusion

Integrating Hadoop with Machine Learning and AI enables businesses to process vast amounts of data efficiently, train advanced models, and deploy AI solutions at scale. With Apache Spark, Mahout, TensorFlow, and PyTorch, organizations can unlock the full potential of big data and artificial intelligence.

As technology evolves, Hadoop’s role in AI-driven data processing will continue to grow, making it a critical tool for enterprises worldwide.

Want to Learn Hadoop?

If you're looking to master Hadoop and AI, check out Hadoop Online Training or contact Intellimindz for expert guidance.

Would you like any refinements or additional details? 🚀

[programmingandengineering]

CSC 4760/6760 DSCI 4760 Big Data Programming Assignment 4

Problem 1. (100 points) On Spark ML – Please use the provided Decision Tree Machine Learning Algorithm to predict the Test Accuracy on the provided dataset. About the dataset: Iris.csv : the dataset classifies flower species based on their sepal and petal length. There are three classification labels (setosa, versicolor, and virginica) Report: Implementation: Implement a PySpark program to solve…

[govindhtech]

BigQuery Studio From Google Cloud Accelerates AI operations

Google Cloud is well positioned to provide enterprises with a unified, intelligent, open, and secure data and AI cloud. Dataproc, Dataflow, BigQuery, BigLake, and Vertex AI are used by thousands of clients in many industries across the globe for data-to-AI operations. From data intake and preparation to analysis, exploration, and visualization to ML training and inference, it presents BigQuery Studio, a unified, collaborative workspace for Google Cloud’s data analytics suite that speeds up data to AI workflows. It enables data professionals to:

Utilize BigQuery’s built-in SQL, Python, Spark, or natural language capabilities to leverage code assets across Vertex AI and other products for specific workflows.

Improve cooperation by applying best practices for software development, like CI/CD, version history, and source control, to data assets.

Enforce security standards consistently and obtain governance insights within BigQuery by using data lineage, profiling, and quality.

The following features of BigQuery Studio assist you in finding, examining, and drawing conclusions from data in BigQuery:

Code completion, query validation, and byte processing estimation are all features of this powerful SQL editor.

Colab Enterprise-built embedded Python notebooks. Notebooks come with built-in support for BigQuery DataFrames and one-click Python development runtimes.

You can create stored Python procedures for Apache Spark using this PySpark editor.

Dataform-based asset management and version history for code assets, including notebooks and stored queries.

Gemini generative AI (Preview)-based assistive code creation in notebooks and the SQL editor.

Dataplex includes for data profiling, data quality checks, and data discovery.

The option to view work history by project or by user.

The capability of exporting stored query results for use in other programs and analyzing them by linking to other tools like Looker and Google Sheets.

Follow the guidelines under Enable BigQuery Studio for Asset Management to get started with BigQuery Studio. The following APIs are made possible by this process:

To use Python functions in your project, you must have access to the Compute Engine API.

Code assets, such as notebook files, must be stored via the Dataform API.

In order to run Colab Enterprise Python notebooks in BigQuery, the Vertex AI API is necessary.

Single interface for all data teams

Analytics experts must use various connectors for data intake, switch between coding languages, and transfer data assets between systems due to disparate technologies, which results in inconsistent experiences. The time-to-value of an organization’s data and AI initiatives is greatly impacted by this.

By providing an end-to-end analytics experience on a single, specially designed platform, BigQuery Studio tackles these issues. Data engineers, data analysts, and data scientists can complete end-to-end tasks like data ingestion, pipeline creation, and predictive analytics using the coding language of their choice with its integrated workspace, which consists of a notebook interface and SQL (powered by Colab Enterprise, which is in preview right now).

For instance, data scientists and other analytics users can now analyze and explore data at the petabyte scale using Python within BigQuery in the well-known Colab notebook environment. The notebook environment of BigQuery Studio facilitates data querying and transformation, autocompletion of datasets and columns, and browsing of datasets and schema. Additionally, Vertex AI offers access to the same Colab Enterprise notebook for machine learning operations including MLOps, deployment, and model training and customisation.

Additionally, BigQuery Studio offers a single pane of glass for working with structured, semi-structured, and unstructured data of all types across cloud environments like Google Cloud, AWS, and Azure by utilizing BigLake, which has built-in support for Apache Parquet, Delta Lake, and Apache Iceberg.

One of the top platforms for commerce, Shopify, has been investigating how BigQuery Studio may enhance its current BigQuery environment.

Maximize productivity and collaboration

By extending software development best practices like CI/CD, version history, and source control to analytics assets like SQL scripts, Python scripts, notebooks, and SQL pipelines, BigQuery Studio enhances cooperation among data practitioners. To ensure that their code is always up to date, users will also have the ability to safely link to their preferred external code repositories.

BigQuery Studio not only facilitates human collaborations but also offers an AI-powered collaborator for coding help and contextual discussion. BigQuery’s Duet AI can automatically recommend functions and code blocks for Python and SQL based on the context of each user and their data. The new chat interface eliminates the need for trial and error and document searching by allowing data practitioners to receive specialized real-time help on specific tasks using natural language.

Unified security and governance

By assisting users in comprehending data, recognizing quality concerns, and diagnosing difficulties, BigQuery Studio enables enterprises to extract reliable insights from reliable data. To assist guarantee that data is accurate, dependable, and of high quality, data practitioners can profile data, manage data lineage, and implement data-quality constraints. BigQuery Studio will reveal tailored metadata insights later this year, such as dataset summaries or suggestions for further investigation.

Additionally, by eliminating the need to copy, move, or exchange data outside of BigQuery for sophisticated workflows, BigQuery Studio enables administrators to consistently enforce security standards for data assets. Policies are enforced for fine-grained security with unified credential management across BigQuery and Vertex AI, eliminating the need to handle extra external connections or service accounts. For instance, Vertex AI’s core models for image, video, text, and language translations may now be used by data analysts for tasks like sentiment analysis and entity discovery over BigQuery data using straightforward SQL in BigQuery, eliminating the need to share data with outside services.

Read more on Govindhtech.com

[sql-datatools]

DataBricks — How to Sum Up Multiple Columns in Dataframe By Using PySpark

DataBricks — How to Sum Up Multiple Columns in Dataframe By Using PySpark https://youtu.be/5Jjls-ovvBs?feature=shared via @YouTube #bigdata #python #dataenginnering #datascience #dataanalytics #ml #ai #digitalanalytics #analytics #learning #programming #cloud #computing #etl

[technical-shorts-datatools]

DataBricks — Transpose OR Pivot OR Rows to Columns in Dataframe By Using...

If you are working as a #PySpark developer, data engineer, data analyst, or data scientist for any organization requires you to be familiar with dataframes because data manipulation is the act of transforming, cleansing, and organising raw data into a format that can be used for analysis and decision making. #bigdata #python #dataenginnering #datascience #dataanalytics #ml #ai #digitalanalytics #analytics #learning #programming #cloud #computing #etl

[myprogrammingsolver]

CSC 4760/6760 DSCI 4760 Big Data Programming Assignment 4

Problem 1. (100 points) On Spark ML – Please use the provided Decision Tree Machine Learning Algorithm to predict the Test Accuracy on the provided dataset. About the dataset: Iris.csv : the dataset classifies flower species based on their sepal and petal length. There are three classification labels (setosa, versicolor, and virginica) Report: Implementation: Implement a PySpark program to solve…

[dataplusweb-blog]

Dataiku : tout savoir sur la plateforme d'IA "made in France"

Dataiku :

tout savoir sur la plateforme d'IA "made in France"

Antoine Crochet-Damais

JDN

 

Dataiku est une plateforme d'intelligence artificielle créée en France en 2013. Elle s'est imposée depuis parmi les références mondiales des studios de data science et de machine learning.

SOMMAIRE

Dataiku, c’est quoi ?

Dataiku DSS, qu'est-ce que c'est ?

Quelles sont les fonctionnalités de Dataiku ?

Quel est le prix de Dataiku ?

Qu’est-ce que Dataiku Online ?

Dataiku Academy : formation / certification

Dataiku vs DataRobot

Dataiku vs Alteryx

Dataiku vs Databricks

Dataiku Community

Dataiku, c’est quoi ?

Dataiku est une plateforme de data science d'origine française. Elle se démarque historiquement par son caractère très packagé et intégré. Ce qui la met à la portée aussi bien des data scientists confirmés que débutants. Grâce à son ergonomie, elle permet de créer un modèle en quelques clics, tout en industrialisant en toile de fonds l'ensemble de la chaine de traitement : collecte, préparation des données…

Co-fondée en 2013 à Paris par Florian Douetteau, son CEO actuel, et Clément Stenac (tous deux anciens d'Exalead) aux côtés de Thomas Cabrol et Marc Batty, Dataiku affiche une croissance fulgurante. Dès 2015, la société s'implante aux Etats-Unis. Après une levée de 101 millions de dollars en 2018, Dataiku boucle un tour de table de 400 millions de dollars en 2021 pour une valorisation de 4,6 milliards de dollars. L'entreprise compte plus de 1000 salariés et plus de 300 clients parmi les plus grands groupes mondiaux. Parmi eux figurent les sociétés françaises Accor, BNP Paribas, Engie ou encore SNCF.

Dataiku DSS, qu'est-ce que c'est ?

Dataiku DSS (pour Dataiku Data Science Studio) est le nom de la plateforme d'IA de Dataiku.

Quelles sont les fonctionnalités de Dataiku ?

La plateforme de Dataiku compte environ 90 fonctionnalités que l'on peut regrouper en plusieurs grands domaines :

L'intégration. La plateforme s'intègre à Hadoop, Spark, mais aussi aux services des clouds AWS, Azure, Google Cloud. Au total, la plateforme est équipée de plus de 25 connecteurs. 

Les plugins. Une galerie de plus de 100 plugins permet de bénéficier d'applications tierces dans de nombreux domaines : traduction, NLG, météo, moteur de recommandation, import/export de données...

La data préparation / data ops. Une console graphique gère la préparation des données. Les time series et données géospatiales sont supportées. Plus de 90 data transformers prépackagés sont disponibles. 

Le développement. Dataiku prend en charge les notebooks Jupyter, les langages Python, R, Scala, SQL, Hive, Pig, Impala. Il supporte PySpark, SparkR et SparkSQL.

Le machine Learning. La plateforme inclut un moteur d'automatisation du machine learning (auto ML), une console de visualisation pour l'entrainement des réseaux de neurones profonds, le support de Scikit-learn et XGBoost, etc.

La collaboration. Dataiku intègre des fonctionnalités de gestion de projet, de chat, de wiki, de versioning (via Git)...

La gouvernance. La plateforme propose une console de monitoring des modèles, d'audit, ainsi qu'un feature store.

Le MLOps. Dataiku gère le déploiement de modèles. Il prend en charge les architecture Kubernetes mais aussi les offres de Kubernetes as a Service d'AWS, Azure et Google Cloud.

La data visualisation. Une interface de visualisation statistique est complétée par 25 graphiques de data visualisation pour identifier les relations et aperçus au sein des jeux de données.

Dataiku est conçu pour gérer graphiquement des pipelines de machine learning. © JDN / Capture

Quel est le prix de Dataiku ?

Dataiku propose une édition gratuite de sa plateforme à installer soi-même. Baptisée Dataiku Free, elle se limite à trois utilisateurs, mais donne accès à la majorité des fonctionnalités. Elle est disponible pour Windows, Linux, MacOS, Amazon EC2, Google Cloud et Microsoft Azure. 

Pour aller plus loin, Dataiku commercialise trois éditions dont les prix sont disponibles sur demande : Dataiku Discover pour les petites équipes, Dataiku Business pour les équipes de taille moyenne, et Dataiku Enterprise pour déployer la plateforme à l'échelle d'une grande entreprise.

Qu’est-ce que Dataiku Online ?

Principalement conçu pour de petites structures, Dataiku Online permet de gérer les projets de data science à une échelle modérée. Il s’agit d’un dispositif de type SaaS (Software as a Service). Les fonctionnalités sont similaires à Dataiku, mais le paramétrage et le lancement de l’application sont plus rapides.

Dataiku Academy : formation et certification Dataiku

La Dataiku Academy regroupe une série de formations en ligne à la plateforme de Dataiku. Elle propose un programme Quicks Start qui permet de commencer à utiliser la solution en quelques heures, mais aussi des sessions Learning Paths pour acquérir des compétences plus avancées. Chaque programme permet de décrocher une certification Dataiku : Core Designer Certificate, ML Practitioner Certificate, Advanced Designer Certificate, Developer Certificate et MLOps Practitioner Certificate.

Dataiku prend en charge les time series et données géospatiales. © JDN / Capture

Dataiku vs DataRobot

Créé en 2012, l'américain DataRobot peut être considéré comme le pure player historique du machine learning automatisé (auto ML). Un terrain sur lequel Dataiku s'est positionne plus tard. Au fur et à mesure de leur développement, les deux plateformes tendent désormais à être de plus en plus comparables.

Face à DataRobot, Dataiku se distingue cependant sur le front de la collaboration. L'éditeur multiplie les fonctionnalités dans ce domaine : wiki, partage de tableaux de bord de résultats, système de gestion des rôles et de traçabilité des actions, etc.

Dataiku vs Alteryx

Alors que Dataiku est avant tout une plateforme de data science orientée machine learning, Alteryx, lui, se positionne comme un solution d'intelligence décisionnelle ciblant potentiellement tout décideur d'entreprise, bien au-delà des équipes de data science.

La principale valeur ajoutée d'Alteryx est d'automatiser la création de tableaux de bord analytics. Des tableaux de bord qui pourront inclure des indicateurs prédictifs basés sur des modèles de machine learning. Dans cet optique, Alteryx intègre des fonctionnalités de machine learning automatisé (auto ML) pour permettre aux utilisateurs de générer ce type d'indicateur. C'est son principal point commun avec Dataiku.

Dataiku vs Databricks

Dataiku et Databricks sont des plateformes très différentes. La première s'oriente vers la data science, la conception et le déploiement de modèles de machine learning. La seconde se présente sous la forme d'une data platform universelle répondant à la fois aux cas d'usage orientés entrepôt de données et BI, data lake, mais aussi streaming de données et calcul distribué.

Reste que Databricks s'enrichit de plus en plus de fonctionnalités orientées machine learning. La société de San Francisco a acquis l'environnement de data science low-code / no code 8080 Labs en octobre 2021, puis la plateforme de MLOps Cortex Labs en avril 2022. Deux technologies qu'elle est en train d'intégrer. 

Dataiku Community : tutoriels et documentation

Dataiku Community est un espace d'échange et de documentation pour parfaire ses connaissances sur Dataiku et ses champs d'application. Après inscription, il est possible d'intégrer le forum de discussions.

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[itonlinetraininginusa]

Why everyone should learn Python

The phenomenal potential of Python and its growth in the field of computer science is well-known. With a continuously increasing talented developer base, Python is a popular, easily accessible programming language. Python is a fantastic choice for people to gain an introduction to programming and computer science because of its syntax and design simplicity. Many people prefer to start with python programming training to start their career in the tech industry.  

Because of Python, there is an exponentially growing developer community in areas like data science, machine learning, AI, web development, and more. Python is a programming language that opens programming access to the world. Python is used even as a server-side software language and it is highly scalable. Leading tech giants’ worldwide use Python extensively, this is great for simple prototypes.

Here are some of the reasons why everyone needs to learn Python:

Python is simple to understand and execute

Python was created to maintain what was necessary and eliminate the unnecessary. Compared to most other popular programming languages, Python is simpler to read, write, and learn.

Within a specific region, several developers were surveyed by a well-known search engine about the simplest programming language to learn. Second place went to Python, which some programmers will argue is more of a scripting language than a true programming language. Python has received praise for its excellent readability and clear, accessible syntax. As was already mentioned, Python is user-friendly and approachable for new programmers because of its consistency and simplicity.

Python is extremely versatile.

Python is used in a wide variety of applications like data mining, data science, artificial intelligence, machine learning, web development, and web frameworks. It is even used in embedded systems, graphic design applications, gaming, network development, product development, rapid application development, testing, and automation scripting.

Python is used as a simpler and more productive replacement for languages like C, R, and Java that carry out related functions. For this reason, Python is becoming highly preferred as the main language for many projects.

Opportunities in Data Science and ML.

The most popular programming language for data science in the past has been R. The popularity of Python for data science has grown because its code is thought to be more scalable and easier to maintain than R. This is especially useful for professionals without extensive degrees in statistics or mathematics.

Python packages for data analysis and machine learning have grown significantly during the last several years. They include data understanding and transformation tools like numpy and pandas. An efficient framework for handling massive amounts of data is Tensorflow, and it is used to code machine learning algorithms. PySpark is an API for interacting with Spark.

Without having to learn the complexities of the more complicated R, these libraries allow the average web developer to analyze large data trends.

Python boasts a supportive community.

You'll want to have confidence that you have a community of programmers who will clear your doubts when a problem arises when you're learning a new programming language. This is true, especially after you've finished your boot camp course or degree. Python has the second-largest community on a popular support site, with more than a million repositories, demonstrating that it has a strong and helpful online community.

Also, Python has a strong community forum network where users can discuss anything from workflow to software development. Additionally, Python programmers frequently plan gatherings all around the world to promote a sense of community and knowledge sharing.

Final thoughts

Finally, you need to know that Python programming is in high demand for jobs, which goes hand in hand with lightning-fast career growth. You need to check out free online courses python to get started in Python. According to the number of job postings on one of the main job search portals, it is evident that Python developers are in high demand in today's world. The education sector is also currently noticing the importance of Python and is slowly starting to provide courses to learn Python.

[antongordon]

Data Preparation for Machine Learning in the Cloud: Insights from Anton R Gordon

In the world of machine learning (ML), high-quality data is the foundation of accurate and reliable models. Without proper data preparation, even the most sophisticated ML algorithms fail to deliver meaningful insights. Anton R Gordon, a seasoned AI Architect and Cloud Specialist, emphasizes the importance of structured, well-engineered data pipelines to power enterprise-grade ML solutions.

With extensive experience deploying cloud-based AI applications, Anton R Gordon shares key strategies and best practices for data preparation in the cloud, focusing on efficiency, scalability, and automation.

Why Data Preparation Matters in Machine Learning

Data preparation involves multiple steps, including data ingestion, cleaning, transformation, feature engineering, and validation. According to Anton R Gordon, poorly prepared data leads to:

Inaccurate models due to missing or inconsistent data.

Longer training times because of redundant or noisy information.

Security risks if sensitive data is not properly handled.

By leveraging cloud-based tools like AWS, GCP, and Azure, organizations can streamline data preparation, making ML workflows more scalable, cost-effective, and automated.

Anton R Gordon’s Cloud-Based Data Preparation Workflow

Anton R Gordon outlines an optimized approach to data preparation in the cloud, ensuring a seamless transition from raw data to model-ready datasets.

1. Data Ingestion & Storage

The first step in ML data preparation is to collect and store data efficiently. Anton recommends:

AWS Glue & AWS Lambda: For automating the extraction of structured and unstructured data from multiple sources.

Amazon S3 & Snowflake: To store raw and transformed data securely at scale.

Google BigQuery & Azure Data Lake: As powerful alternatives for real-time data querying.

2. Data Cleaning & Preprocessing

Cleaning raw data eliminates errors and inconsistencies, improving model accuracy. Anton suggests:

AWS Data Wrangler: To handle missing values, remove duplicates, and normalize datasets before ML training.

Pandas & Apache Spark on AWS EMR: To process large datasets efficiently.

Google Dataflow: For real-time preprocessing of streaming data.

3. Feature Engineering & Transformation

Feature engineering is a critical step in improving model performance. Anton R Gordon utilizes:

SageMaker Feature Store: To centralize and reuse engineered features across ML pipelines.

Amazon Redshift ML: To run SQL-based feature transformation at scale.

PySpark & TensorFlow Transform: To generate domain-specific features for deep learning models.

4. Data Validation & Quality Monitoring

Ensuring data integrity before model training is crucial. Anton recommends:

AWS Deequ: To apply statistical checks and monitor data quality.

SageMaker Model Monitor: To detect data drift and maintain model accuracy.

Great Expectations: For validating schemas and detecting anomalies in cloud data lakes.

Best Practices for Cloud-Based Data Preparation

Anton R Gordon highlights key best practices for optimizing ML data preparation in the cloud:

Automate Data Pipelines – Use AWS Glue, Apache Airflow, or Azure Data Factory for seamless ETL workflows.

Implement Role-Based Access Controls (RBAC) – Secure data using IAM roles, encryption, and VPC configurations.

Optimize for Cost & Performance – Choose the right storage options (S3 Intelligent-Tiering, Redshift Spectrum) to balance cost and speed.

Enable Real-Time Data Processing – Use AWS Kinesis or Google Pub/Sub for streaming ML applications.

Leverage Serverless Processing – Reduce infrastructure overhead with AWS Lambda and Google Cloud Functions.

Conclusion

Data preparation is the backbone of successful machine learning projects. By implementing scalable, cloud-based data pipelines, businesses can reduce errors, improve model accuracy, and accelerate AI adoption. Anton R Gordon’s approach to cloud-based data preparation enables enterprises to build robust, efficient, and secure ML workflows that drive real business value.

As cloud AI evolves, automated and scalable data preparation will remain a key differentiator in the success of ML applications. By following Gordon’s best practices, organizations can enhance their AI strategies and optimize data-driven decision-making.