Programming etc

3 big data tools. DataIQ, Databricks, Delta Lake

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user1272047
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Table of contents

Intro.

1. DataIQ

  1. Data Governance & Intelligence – DataIQ is a platform that helps organizations manage, track, and govern their data assets. It provides insights into data quality, lineage, and compliance, ensuring regulatory adherence while optimizing data usage.

  2. Data Cataloging & Discovery – It enables businesses to create searchable catalogs, making data easily discoverable across departments. Automated metadata collection helps classify and enrich datasets for better usability.

  3. Collaboration & Automation – DataIQ supports team collaboration by allowing users to share insights, annotations, and workflows. It also integrates automation tools to streamline data preparation, reducing manual efforts in data processing.

2. Databricks

  1. Unified Data & AI Platform – Databricks combines data engineering, data science, and machine learning in a collaborative cloud-based environment. Built on Apache Spark, it enables large-scale data processing with optimized performance.

  2. Lakehouse Architecture – It merges data lakes and data warehouses, providing a single platform for both structured and unstructured data. This ensures real-time analytics, governance, and cost-efficiency without data silos.

  3. Scalability & Collaboration – Databricks supports multi-cloud deployment (AWS, Azure, GCP) and allows teams to work together on notebooks, dashboards, and workflows, enhancing productivity and data accessibility.

3. Delta Lake

  1. Reliable Data Storage Layer – Delta Lake is an open-source storage layer that enhances data lakes with ACID transactions, ensuring consistency and reliability in big data workflows. It prevents data corruption and incomplete writes.

  2. Schema Evolution & Time Travel – It supports schema changes dynamically without breaking pipelines and enables rollback to previous data versions, ensuring traceability and auditability of datasets.

  3. Performance Optimization – Delta Lake improves query performance using indexing, caching, and compaction techniques, reducing latency and enabling faster data processing for analytics and machine learning applications.

Task example.

1. DataIQ

  1. Create a Data Catalog – Use DataIQ to scan and index datasets across the organization, automatically tagging metadata and classifications to improve data discoverability and governance.

  2. Monitor Data Quality – Set up automated rules to track anomalies, inconsistencies, and missing values, ensuring high data integrity for analytics and compliance.

2. Databricks

  1. Build a Machine Learning Model – Use Databricks notebooks to preprocess data, train a machine learning model using Spark MLlib, and deploy it for real-time predictions.

  2. Perform ETL on Big Data – Leverage Databricks to ingest raw data from multiple sources, clean and transform it using Apache Spark, and store it in a Delta Lake for analytics.

3. Delta Lake

  1. Implement Time Travel Queries – Use Delta Lake’s time travel feature to retrieve historical data, compare different dataset versions, and restore previous states when needed.

  2. Optimize Data Lake Performance – Run compaction and indexing jobs to reduce small file issues, improve query speed, and maintain efficient storage for analytical workloads.

Example Projects:

  1. Developer of a Web2 Crypto App, MongoDB, AWS, Betting.

    • Data Engineering: Data Ingestion & ETL, Data Warehousing, Feature Engineering, Model Training & Evaluation.

    • Backend: Python, Java, Node.js.

    • DevOps: Docker, Kubernetes, Terraform, AWS.

    • Tools: Scipy, XGBoost, Pandas, SQL.

    • DataIQ: Metadata Management, Data Discovery

    • Databricks: ETL Pipelines, ML Deployment

    • Delta Lake: Version Control, Schema Evolution

Example Projects:

  1. Artificial Intelligence and Computer Vision; Web API.

    • Data Science: Exploratory Data Analysis (EDA), Predictive Analytics, AI Model Training.

    • Backend: Python, Java, Node.js.

    • DevOps: Kubernetes, Terraform, AWS.

    • Tools: PyTorch, TensorFlow, Hugging Face, OpenCV.

    • DataIQ: Data Lineage, Access Control

    • Databricks: Deep Learning, Distributed Training

    • Delta Lake: Rollback Data, Time Travel

  2. Stock price prediction/classification; Web API; Scraping.

    • Data Engineering: Big Data Processing (Spark, Flink), Data Pipeline Automation, Database Management.

    • Backend: Python, Java, Node.js.

    • DevOps: Docker, Kubernetes, AWS.

    • Tools: Snowflake, LightGBM, CatBoost, RAPIDS.

    • DataIQ: Data Classification, Governance Policies

    • Databricks: Real-time Streaming, Forecasting Models

    • Delta Lake: Data Integrity, Performance Optimization

  3. Recommendation System for E-Commerce; Web API; CRUD.

    • Data Science: Machine Learning Deployment, Real-time Data Processing, A/B Testing.

    • Backend: Python, Java, Node.js.

    • DevOps: EC2, Kubernetes, Terraform, Ansible.

    • Tools: Spark, Snowflake, Statsmodels, H2O.

    • DataIQ: Data Compliance, Risk Analysis

    • Databricks: Feature Engineering, Large-Scale Processing

    • Delta Lake: Optimized Storage, Query Acceleration

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