Hire the Best Big Data Developers
Kathmandu, Nepal
I'm a Senior Big Data and AI Engineer who builds production data platforms at petabyte scale and agentic AI systems that operate autonomously in production. Over the past nine years, I've worked across AWS, GCP, and Azure, delivering 50% faster pipelines, 40% cost reductions, and measurable automation of analytics workflows through LangGraph, Google ADK, and Databricks. On Upwork that translates into 21 completed contracts with near-perfect 5-star ratings and repeat enterprise clients โ from quick, high-stakes database fixes to 100+ hour platform engagements. I take full ownership: architecture, cloud infrastructure, and delivery. WHAT CLIENTS HIRE ME FOR โข Databases & DBA (deep specialty) โ PostgreSQL, Citus, TimescaleDB, ClickHouse, and Trino: cluster design, replication and high availability, performance tuning, and migrations with zero data loss (RDS-to-Citus, 500GB+ PostgreSQL, ClickHouse shard/replication design). This is where my reviews are densest: - "His knowledge of ClickHouse Cluster is truly exceptional. He designed it exactly as needed." - "A game-changer โ his analysis identified cost-saving opportunities and improved our solution architecture." (AWS database review) - "Super helpful even though it was new technology that's hard to find talent for." (pgvector on RDS Postgres) - "Did a fantastic job improving the database speed and migration." (500GB PostgreSQL migration) โข Big-Data Platforms & Pipelines โ Apache Spark, Trino, Apache Iceberg/Delta Lake, and Airflow + dbt on Kubernetes. I design petabyte-scale batch and streaming pipelines with the governance, lineage, observability, and SLOs production demands โ typically 50% faster processing, 40% lower cost, and 99.9% uptime. โข Production GenAI & Autonomous Agents โ agentic systems on Google-ADK, LangChain, LangGraph, CrewAI, and MCP, plus RAG pipelines on Milvus, pgvector, and Pinecone. I ship them with real tracing and observability (LangFuse, Opik, MLflow) and guardrails โ systems that run reliably, not demos. RECENT WORK โข ClickHomes AI โ a live real-estate analytics and lead-generation platform I architected end to end: a dual database (ClickHouse OLAP + PostgreSQL OLTP), a RESO-compliant ETL pipeline ingesting 9,500+ MLS listings with schema validation and lineage, a LangGraph ReAct AI search agent with streaming responses, multi-factor lead scoring, and Celery-powered email drip campaigns โ deployed on Docker/Nginx and live in production. โข TARA @ UXCam โ an autonomous multi-agent analytics platform (Google-ADK + MCP) across 500+ mobile apps that cut analytics delivery time by 60% and manual effort by 75%, with RAG over 1M+ videos a month and full agent observability, governing 10TB+ of data per day on Databricks + Unity Catalog. HOW I WORK I design the architecture, stand up the cloud infrastructure, and deliver production-ready code โ then make it observable, reliable, and cost-efficient. I communicate clearly and proactively across time zones, and I'm direct about trade-offs and timelines. TECH STACK Databases: PostgreSQL, Citus, TimescaleDB, ClickHouse, MySQL, MongoDB, Redis. Data: Apache Spark, Trino, Apache Iceberg, Delta Lake, Airflow, dbt, Kafka, Kinesis. AI/GenAI: Google-ADK, LangChain, LangGraph, CrewAI, AutoGen, MCP, RAG, Milvus, pgvector, Pinecone, Weaviate, MLflow, LangFuse, Opik. Cloud & DevOps: AWS, GCP, Azure, Kubernetes, Docker, Terraform, CI/CD, Prometheus, Grafana. Languages: Python, SQL, FastAPI, Next.js. Education: B.Tech in Computer Science. If you're building or scaling a data platform, fixing a database bottleneck, or putting an AI agent into production, send me a short brief โ I'll reply with a concrete plan, a timeline, and the trade-offs that matter.
- Big Data
- Data Migration
- Database Design
- Database Optimization
- SQL
- PostgreSQL
- ETL
- MySQL
- Python
- Database Administration
- Apache Spark
- DevOps
- Linux System Administration
- Kubernetes
- Database Architecture
- Django
Indore, India
๐ TOP RATED PLUS Data Expert | Top 3% on Upwork ๐ฐ $600K+ earned | 16,000+ hours | 130+ clients served I am a Sr Data Engineer with expertise in developing robust AI Agents & Analytics layer. I bring over 8 years of hands-on experience with: - Building scalable ETL data pipelines that fetch raw data froms APIs and store it into data warehouses hosted over GCP/AWS (BigQuery | Snowflake | Redshift). - Developing Business Intelligence reports and dashboards using Data Studio (formerly Looker Studio), Metabase, Looker, Tableau etc. - Building powerful AI Agents using Gemini, Claude, OpenAI, Dialogflow. - Track user behaviour data using GA4 and Google Tag Manager. Iโve worked with 130+ clients across eCommerce (Shopify, WooCommerce), digital marketing & paid ads (Meta, Google Ads), mobile apps & gaming analytics, SaaS & web apps, and data-driven businesses in education, clean energy & media. ๐ก What I do (End-to-End Ownership) 1. Data Engineering & Warehousing: - Build scalable, reliable data pipelines using BigQuery, Snowflake, Redshift, Python and APIs of data sources - Automated ETL (Airflow, APIs, Fivetran, custom Python scripts) - Single source of truth across marketing + product + revenue 2. Analytics & BI (Decision Systems, not just dashboards): - Executive dashboards (Looker, Data Studio (formerly Looker Studio), Metabase, Power BI, Tableau) - KPI frameworks aligned to revenue - Cohort, LTV, attribution & funnel analysis 3. Marketing & Web Tracking (Accuracy = $$$) - GA4, Google Tag Manager, Server-side tracking - Meta CAPI, Google Ads, TikTok tracking - Fix broken attribution & data loss 4. Generative AI & Automation - AI agents & workflows (OpenAI, Gemini, Claude) - Automate reporting, insights, and ops - Use AI where it actually improves ROI (not hype) ๐ Real Outcomes Iโve Delivered โ Built full marketing data warehouse โ improved spend efficiency by 30%+ โ Fixed tracking & attribution โ recovered lost revenue visibility โ Automated reporting โ saved 20+ hrs/week for teams โ Delivered exec dashboards โ faster, data-backed decisions ๐ง Why Clients Choose Me - I think like a business owner, not just an engineer - I focus on revenue impact, not vanity metrics - I handle end-to-end (tracking โ pipelines โ dashboards โ insights) - Strong communication + fast execution (no hand-holding needed) ๐ My Tech Stack: - Business Intelligence & Data Visualisation: Google Data Studio (formerly Looker Studio) , Looker, Metabase, Power BI, Mode, Tableau, Databox, Zoho Analytics, DOMO, Google Sheets, etc. - AI: LLMs like OpenAI, ChatGPT, Gemini, Claude, DeepSeek, and GCP's services like Document AI, DialogFlow, CCAI, etc. - Engineering: SQL, Python, Airflow, APIs, Cloud Functions, Lambda Functions, Cloud Composer, Cloud Run - Data Warehouses: BigQuery, Redshift, MS SQL, MySQL, PostgreSQL, Snowflake, and Azure. - ETL & Webhook tools: n8n, Fivetran, Stitch, Windsor, Supermetrics, Power My Analytics, Saras Analytics, Zapier, Make, etc. - Tracking: Google Tag Manager, Google Analytics 4, Meta Ads Conversion API, Google Ads Conversion tracking, Stape, Server-side tracking. - Data Sources: Shopify, WooCommerce, BigCommerce, Meta Ads, Google Ads, TikTok Ads, Pinterest Ads, LinkedIn Ads, Apple Ads, Amazon Ads, Bing Ads, Google Analytics 4, Google Search Console, Google My Business, HubSpot, Active Campaign, PipeDrive, Facebook Page Insights, Instagram Insights, Stripe, SEMRush, MailChimp, Klaviyo, ClickUp, Ahref, etc. ๐ Letโs Work If youโre looking for someone who can own your entire data stack and turn it into a revenue engine, letโs talk. Click โInviteโ and letโs discuss your use case ๐ ----- ๐ ๐๐๐ฌ๐ช๐ข๐ฅ๐๐ฆ GA4, Google Analytics 4, Google Tag Manager (GTM), Server-side Tracking, Meta Conversion API (CAPI), Google Ads Conversion Tracking, Marketing Attribution, BigQuery, Snowflake, Redshift, Data Warehouse, Big Data, Data Engineering, ETL, ELT, Data Pipelines, Apache Airflow, Airflow DAGs, PySpark, Spark, Databricks, SQL, Python, Advanced SQL, Data Modeling, Data Transformation, Data Architecture, Data Lakes, Data Studio, Looker Studio, Power BI, Tableau, Data Visualization, Dashboard Development, Business Intelligence (BI), KPI Dashboard, Reporting Automation, Shopify Analytics, WooCommerce Analytics, Marketing Analytics, Product Analytics, Funnel Analysis, Cohort Analysis, LTV Analysis, Retention Analysis, Generative AI, OpenAI, ChatGPT, Gemini, Claude, AI Agents, AI Automation, AI Agents, LLM Applications, n8n, Workflow Automation, No-code Automation, Low-code Automation, Zapier, Make (Integromat), API Integrations, Webhooks, Stripe, HubSpot, Google Ads, Meta Ads, TikTok Ads, LinkedIn Ads, Cloud Platforms (GCP, AWS), Cloud Functions, AWS Lambda, Data Orchestration
- Big Data
- Amazon Web Services
- Apache Airflow
- BigQuery
- Business Intelligence
- Python
- Google Tag Manager
- SQL
- Data Science
- Looker Studio
- Google Cloud Platform
- Artificial Intelligence
- Data Engineering
- ETL Pipeline
- Data Warehousing
- Data Visualization
- Tableau
- Generative AI
- AWS Lambda
- Amazon Redshift
Barcelona, Spain
โ Top 1% part of Upwork's Expert-Vetted program | NO AGENCY SOLO DEVELOPER ๐๏ธ 8 years+ of experience in Data Science ๐ 180+ Upwork Projects ๐ฏ Less than 1 Hour Response time My specialty is to take your business problem and find a suitable end to end solution using AI and programming tools from Python, R, JavaScript programming languages. My extensive experience and wide skillset from data acquisition, model training to production grade application or REST API development will save you time and costs (material or psychological i.e. trying to find developers to make MVP, organize and support communication within team). AI Agentic systems are overtaking markets with potential impact on various businesses and creating opportunities to utilize. My skills in AI Agentic system development using Langchain, LangGraph, CrewAI, Autogen, RAG, MCP, Retell.ai, Elevenlabs will give you opportunities to cut costs and optimize your business operations. My skills include machine learning, deep learning, computer vision, web scraping, data engineering, web development, and data visualizations. I can create interactive web applications and dashboards using Python's Dash framework and R's shiny package so you will be able to observe, analyze and present various aspects of your business and other activities in practical ways. My expertise also includes the development of graphical user interface GUIs with Python's Kivy framework. In Computer Vision, my skills include image classification, object detection, and image segmentation with Python tools such as Tensorflow, Keras, CNNs (LeNet, AlexNet, VGG1619, InceptionV3, ResNet50), SSD, YOLO, TFOD, and Mask R-CNN. Importantly, I have skills in math and statistics essential for understanding processes behind code and interpreting outcomes from it. I have done my MBA with a focus on data science. I have been working as an accountant for around five years, including a member of the Big Four and as Data Scientist in a local IT company focused on DS. Thus, I understand finance from theoretical and practical sides and can apply code to analyze vast amounts of financial or other data efficiently. Considering my previous experience, my domain knowledge in finance, marketing (CTR, CLV), process optimization, and other business areas, I will focus on understanding your business goals and implementing solutions to make them come true. My skills include: โ Data Science โ Machine Learning โ Deep Learning โ Algorithmic Trading โ Generative AI ( Langchain, IIamaIndex, LangGraph, LangSmith, HuggingFace, StableDiffusion, Midjourney, OPEN AI, CHAT GPT4, CHAT GPT3.5, Mistral7B, Gemini, Cursor, Ollama, CrewAI, AutoGen, MCP, Google MCP Toolbox for Databases) โ Prompt Engineering (ICO, TESSA, ReAct, Chain of Thought, Map Reduce, Refine) โ AI Agents, Inbound & Outbound & Batch Calls, Chatbots, RAG, Conversational Agents, VAPI, Retell.ai, make.com, gohighlevel, n8n, telnyx, twilio โ Full Stack Development (React, React Native, Next.js) for AI integration โ Interactive Visualizations/Dashboards โ Data Engineering (MySQL, MongoDB, PostgreSQL, BigQuery, Oracle, SQLServer, Pinecone, ETL) โ Python, R, SQL โ Object Oriented Programming (OOP) โ PEP-8 (pylint, isort, flake8, autopep, black, docstrings, pydocstyle, mkdocs) โ Web development (interactive dashboards Dash) โ Bot Development (Telegram) โ Graphical User Interfaces (GUI) Kivy โ Big Data (Spark) โ Recommender Systems โ DevOps (ML Deep Learning model deployment on cloud, TDD, AWS, GCP, Azure, Docker, REST API) โ OCR โ Computer Vision โ Audio Processing โ Natural Language Processing NLP (Bert, fastText, Langchain) โ CNN (LeNet, AlexNet, VGG1619, InceptionV3, ResNet50) โ Object Detection (R-CNN, SSD, YOLO, TFOD API) โ Image Segmentation (Mask R-CNN) โ Transfer Learning โ Time Series Analysis (ARIMA, SARIMA, LSTM, PROPHET) โ OpenCV โ Educational Tutorials โ Web Scraping โ Web Crawling โ API Clients โ Keras, Tensorflow, PyTorch โ Dash, Shiny, Plotly, Streamlit, React, Next.js โ Pandas, Numpy, Scipy, Scrapy, Selenium, requests, ggplot2 โ IOT (Raspberry Pi) โ REST API (Google Ads, Google Analytics, FB/META API, Stripe, CCXT, OPEAI etc.) โ REST API development (Flask, FastAPI)
- Python
- R
- Tesseract OCR
- Computer Vision
- Deep Learning
- Data Science
- Machine Learning
- Dash
- R Shiny
- Generative AI
- AI Chatbot
- Stable Diffusion
- React
- Next.js
- React Native
Stockholm, Sweden
Struggling to unlock value from your data or build scalable, high-performance analytics platforms? Iโm ๐ฏ๐๐๐๐๐ ๐จ๐๐๐,a Senior Data Engineer specializing in Databricks, Snowflake, Big Data Engineering, and scalable ETL/ELT solutions. With expertise in PySpark, Python, SQL, GCP, AWS, Azure, and NLP, I build high-performance data pipelines, cloud data platforms, and real-time analytics solutions. Experienced in data warehousing, cloud integration, machine learning workflows, and performance optimization to transform raw data into actionable business insights. Letโs build reliable, scalable, and data-driven solutions for your business growth. Iโve successfully completed 99+ projects across industries, designing ETL pipelines, MLOps workflows, Delta Lake architectures, and cloud analytics solutions on AWS, Azure, and GCP. โ๏ธ ๐ฏ๐๐ ๐ฐ ๐ฏ๐๐๐ ๐ฉ๐๐๐๐๐๐๐๐๐ ๐ป๐๐๐๐๐๐๐๐ ๐ซ๐๐๐ ๐๐๐๐ ๐ฐ๐๐๐๐๐๐๐ โ Databricks & Big Data Engineering I specialize in designing enterprise-grade Databricks Lakehouse architectures and Delta Lake solutions. My expertise in Spark and PySpark allows me to build high-performance pipelines for both batch and real-time analytics, ensuring your data infrastructure is robust and scalable. โ Machine Learning & MLOps With a focus on machine learning and MLOps, I build and deploy predictive models using tools like MLflow and TensorFlow. I automate end-to-end ML pipelines to enhance efficiency and accuracy, driving impactful insights from your data. โ Cloud & Data Platforms I implement secure, scalable cloud solutions on platforms like AWS, Azure, and GCP. My experience includes cloud migration, Kubernetes, Docker, and CI/CD automation, ensuring seamless integration and optimal performance. โ ETL & Data Pipelines I develop reliable ETL processes and data pipelines that streamline data integration and transformation. My work with streaming analytics using Kafka and Spark ensures real-time data processing and actionable insights. โ Data Analyst & Visualization I create actionable dashboards and visualizations using Power BI, Tableau, and Databricks SQL. My focus is on driving KPI reporting and business intelligence to support strategic decision-making. โ Snowflake I leverage Snowflake's capabilities to build efficient data warehousing solutions, optimizing data storage and retrieval for enhanced performance and scalability. โ Python My proficiency in Python allows me to develop complex data processing scripts and machine learning models, ensuring robust and efficient data handling. โ NLP (Natural Language Processing) I apply NLP techniques to extract meaningful insights from unstructured data, enabling advanced text analytics and improved decision-making processes. โ GCP (Google Cloud Platform) I utilize GCP's powerful tools to design and deploy scalable cloud solutions, ensuring high availability and performance for your data-driven applications. โ Data Warehouses I design and manage data warehouses that provide a centralized repository for your data, facilitating efficient data analysis and reporting. โ๏ธ ๐ฒ๐๐ ๐ป๐๐๐๐ & ๐ป๐๐๐๐๐๐๐๐๐๐๐ โช Databricks & Big Data: Databricks, Delta Lake, Apache Spark, PySpark, Unity Catalog, Kafka, Hadoop, Real-time Streaming โช Machine Learning: MLflow, TensorFlow, PyTorch, scikit-learn, Feature Store, Predictive Analytics, NLP โช Cloud Platforms: AWS, Azure, GCP, Kubernetes, Docker, CI/CD โช Analytics & BI: Power BI, Tableau, Databricks SQL, KPI Dashboards, Data Strategy โช Data Engineering: ETL Pipelines, Data Lakes, Data Warehousing, Data Migration, Performance Optimization โ๏ธ ๐พ๐๐ ๐ช๐๐๐๐๐ ๐ด๐ I combine deep technical expertise with practical business understanding, delivering scalable, cost-efficient, and AI-ready data solutions. My goal is to turn your data into a strategic asset that powers smarter decisions and measurable growth. Letโs collaborate to build your next-generation analytics platform and unlock the full potential of your data. Check my portfolio for architecture samples, dashboards, and case studies. Databricks Engineer, Big Data Consultant, Spark Developer, MLOps Engineer, Data Engineer, AWS Data Specialist, Azure Databricks, GCP Analytics, ETL Developer, Data Analytics, Delta Lake Expert, Machine Learning Engineer, Python, Database Architecture, Data Processing, ETL, Big Data, Database Design, Data Engineering, Data Analytics & Visualization Software, Data Visualization, Deep Learning Modeling, Data Warehousing & ETL Software, Snowflake, Amazon Web Services, ETL Pipeline, Machine Learning, Deep Learning, Data Science, Data Analysis, Cloud Engineering, Artificial Intelligence, Databricks Engineer, Big Data Consultant, Spark Developer, MLOps Engineer, Data Engineer, AWS Data Specialist, Senior Data Engineer specializing in Databricks, Snowflake, Big Data Engineering, and scalable ETL/ELT solutions. With expertise in PySpark, Python, SQL, GCP, AWS, Azure, and NLP
- Big Data
- Python
- ETL
- Data Engineering
- Snowflake
- Machine Learning
- ETL Pipeline
- Database Architecture
- Data Processing
- Database Design
- Data Analysis
- Cloud Engineering
- Data Analytics & Visualization Software
- Data Warehousing & ETL Software
- BigQuery
- Data Integration
- Databricks Platform
- Database
- Data Analytics
- Apache Flink
Islamabad, Pakistan
๐ Hello, I'm Malik Salman. I specialize in helping businesses stand out by making smarter decisions through data analytics solutions. With over five years of experience and a strong focus on big data, data analysis, and interactive reporting, I bring a deep understanding of how to turn raw data into meaningful insights. My core strengths lie in data modeling and optimization, automation, and building intuitive self-service reporting solutions. ๐ง What I bring to the table: โ Expert-level database design, administration, and SQL programming โ End-to-end data extraction, ETL, and transformation workflows โ Comprehensive data preparation and cleansing โ High-performance data model design and optimization โ Advanced analytics with Power Pivot and DAX โ Dynamic, insightful dashboards in Power BI, Tableau, and Looker Studio โ Workflow automation using Power Automate โ Azure-based app development with Microsoft Power Apps โ Effective training and ongoing support to empower your team ๐ Data Analyst & Business Intelligence Expertise: As a data analyst, visualization specialist and Excel expert, I build scalable dashboards in Power BI, Tableau, Excel. My data analyst approach ensures efficient workflows for every data analyst project. ๐ง Advanced DAX formulas and Excel functions for KPI dashboards ๐ Power BI and Tableau dashboards for operations, sales, finance visualization ๐ Expert visualization in Power BI, Excel, and Tableau highlighting trends ๐ Dashboard development from Excel data extraction to Power BI publishing ๐ Data analyst transformation with Power Query, Excel, Tableau Prep ๐ Database connections (SQL, Snowflake) for Power BI dashboards ๐ API integration (REST, GraphQL) for Power BI and Tableau dashboards ๐ Integrated visualization with Power BI, Excel, Tableau, DAX ๐ Data Analyst Use Cases & Dashboard Experience: ๐งฉ Sales Analytics - Data analyst visualization using Power BI, Excel, Tableau ๐ผ Finance Dashboards - Excel models with Power BI visualization dashboards ๐ฏ Marketing Analytics - Campaign visualization with Tableau and Power BI ๐ก Executive Dashboards - Data analyst visualization for C-level in Power BI, Excel ๐ Geo Analytics - Location visualization using Power BI and Tableau dashboards ๐ Operations Dashboards - Data analyst KPI tracking with Excel and Power BI ๐ Customer Retention - Track metrics with Power BI, Excel, and Tableau dashboards ๐ CRM Analytics - Data analyst, Salesforce data in Tableau, Excel, Power BI ๐ง Data Analyst Tools & Skills โ Data analyst using Power BI, Tableau, Excel, DAX, and Power Query for dashboards โ Data analyst mastery of Excel functions, pivot tables for dashboard techniques โ Tableau Desktop, Prep, and visualization for dashboards โ Excel data analysis with Power Pivot for enhanced dashboards โ DAX formulas for Power BI dashboards and Excel reports โ SQL, PostgreSQL database integration for Power BI dashboards โ Snowflake, Azure SQL connections for data analysis dashboards โ REST API, GraphQL integration for Excel and Power BI dashboards โ JSON data processing for Tableau and Power BI visualization โ Data analyst pipeline automation for Excel and Power BI dashboards โ Excel financial modeling with Power BI visualization โ Tableau calculations for dynamic dashboards โ DAX time intelligence for Power BI dashboards and Excel reports ๐ฌ Let's Connect ๐ฌ Click INVITE to start your project with an expert data analyst. ๐ Ready to discuss your data analyst needs for Power BI, Excel, Tableau, DAX.
- Tableau
- Business Intelligence
- Database
- Data Science
- Sentiment Analysis
- Data Analysis
- Microsoft Power BI
- Data Analysis Expressions
- Exploratory Data Analysis
- Looker
- Google Analytics
- Data Visualization
- Microsoft Power BI Data Visualization
- Azure DevOps
- Microsoft Dynamics CRM
Lahore, Pakistan
A working demo and a system that survives production are two very different things. I build the second kind. Over a decade of backend engineering and 70+ completed projects on Upwork. I run Datum Brain, a software and AI engineering company, so you get one senior engineer accountable end to end, with a team behind him when the work needs it. We ship Go microservices, data pipelines, and LLM systems for FinTech, SaaS, EdTech, and AdTech clients. Recent work: ๐ก Real-time ad fraud detection (TrueAudience) High-concurrency Go backend that scores ad traffic to catch bots before advertisers pay. 80M+ events, under 20ms decisions. Go, TimescaleDB, real-time pipelines. ๐ฆ Go microservices for an EMI and payments platform (Zolvat) Ledger logic, payment flows, and service orchestration for an installment payments platform. Go, microservices, PostgreSQL. ๐ฅ WebRTC proctoring infrastructure (Meazure / Examity) Real-time media and session orchestration for thousands of concurrent exam sessions at one of the largest US proctoring providers. Go, WebRTC, LiveKit, NATS. ๐ Big data pipeline optimization (Nike) Cut processing time and compute cost on large PySpark workloads. PySpark, AWS. ๐ Document intelligence platform (Centrum AI) OCR and RAG pipeline that turns unstructured documents into structured, queryable data with human review. Python, FastAPI, LangChain, RAG. What I work with: Backend: Go, Python, FastAPI, gRPC, REST, PostgreSQL, TimescaleDB, Redis, NATS, Kafka Data: PySpark, ETL pipelines, data quality frameworks, AWS, GCP Real-time: WebRTC, LiveKit, event streaming, high-concurrency systems AI and LLM: LangChain, LangGraph, RAG, multi-agent systems, OpenAI, Claude, evals Infra: Docker, Kubernetes, CI/CD, nginx How I work: I scope before I quote, so you know what you are getting and when. I flag risks early instead of at the deadline. Production-ready means tested, documented, monitored, and handed over properly. Good fit if you need something that runs in production and not a proof of concept, you want one senior engineer accountable end to end, and you value clear communication and honest scoping. Not a fit if you are shopping purely on hourly rate, or the spec changes daily and nobody owns decisions. If your backend needs to handle real load, or your data or AI work needs to go from demo to product, send me a message.
- Big Data
- Python
- Apache Spark
- Golang
- Large Language Model
- Generative AI
- Artificial Intelligence
- OpenAI API
- AI Bot
- ETL Pipeline
- Microservice
- Web Scraping
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What Is Big Data?
While big data has become a trendy catchphrase, the good news is that there is real substance to it. With a little effort, even nontechnical people can understand that substance and start putting it to work for their companies.
Part of demystifying the trendy catchphrase โbig dataโ is understanding that youโre analyzing your business using techniques of statistical analysis, some of which have been around for 50 years or more.
What is fundamentally different about the 21st-century phenomenon of โbig dataโ is the computing power we can bring to bear. Advances in the sensors that collect data, the drives that store it, and the software and hardware to analyze it mean that we can efficiently analyze far more material than was feasible in earlier centuries.
Itโs no longer hard to create and store gigabytes of dataโthe challenge is to find something meaningful in all of that material. What makes analyzing the data such a rich source of business insights?
Big data is good at finding correlations but not at causality
A great place to start is with the distinction between โwhat you likeโ and โwhy you like itโโor what is technically called the difference between correlation and causality. These algorithms donโt know why you like what you like. But they have learned what you will like based on what youโve purchased before.
From a business perspective, thatโs OKโwhat matters far more than why. Knowing what you will like drives clicks and sales. Skilled data scientists have a host of statistical techniquesโsome new, some oldโfor analyzing information. Before you start working with a data scientist, however, thereโs an important question you need to ask first.
Whatโs the type of dataset you want to learn more about?
If you donโt ask this all-important question, you could get overwhelmed with raw data. Many executives feel pressure to just do something with big data, so they begin collecting without a clear goal in mind.
If you do โtrack everything,โ youโll still have to go through that data again once you figure out what youโre trying to do. And in the meantime, youโll be racking up software, hardware, and personnel costs.
A key takeaway? Donโt just rush in and start tracking everything. The best way to get started is to look at the types of problems people have successfully attacked with big data in order to see what you might accomplish in your business. Here are a few examples:
- Branding: Look at mentions of a product on Twitter in order to derive an analysis of โcustomer sentiment.โ By collecting mentions of your brand from Twitter, data scientists not only can tell how customers feel about it but also how strongly they feel about it. Data scientists can also then help you automate your responses: re-tweeting of positive comments, and prompt, private messages to unhappy customers.
- Market research: Analyze your past sales records to segment your customer base so that you can find and target like-minded clusters of people with carefully customized marketing campaigns.
- Operations: Analyze the geolocation data of your delivery drivers to optimize the most efficient routes in terms of gasoline usage and time. Data scientists can compare up-to-the-minute data about where your vans are on the road with historical data about what routes are congested with vehicles or require time-consuming left-hand turns across traffic.
- Production optimization: A large beverage company used data to find the optimal blend of different kinds of oranges, which have different costs, astringency, sweetness, and tartness, in order to maximize profit while maintaining quality standards.
- Research: A large hedge fund hired researchers to keep track of real-time news on 200 companies at a time. The team was spending so much time seeking data, like looking for company press releases, regulatory sites, SEC filings, and updates to company websites, that they couldnโt keep up with all of the changes. Data consultancy BrightPlanet put together an algorithm to search the Internet and compile information automatically, freeing up the team to focus on analyzing the findings.
Tips for analyzing big data
There are some unusual features of massive datasets that you should keep in mind.
1. The โmessinessโ of big data
You may be surprised by how much time your consultants are using on a stage of the project called โdata preparation.โ Donโt be. Because computers, databases, and algorithms have gotten so fast, getting large datasets, often disorganized and drawn from multiple sources, in a position to be analyzed is quite challenging. โ
Data scientists unabashedly describe their datasets as โmessy.โ (Thatโs really the technical term for it.) Imagine, for example, you tell a web-crawling algorithm to compile massive amounts of press releases, tweets, news reports, and government filings from different websites and in different formats. The results from the web-crawling algorithm are not going to consist of neat, well-organized rows in a spreadsheet or fields in a database.
This โunstructuredโ data will need to be โcleanedโ or made uniform in a way that algorithms can analyze. Thatโs why โdata preparationโ often takes so much time.
2. You donโt need to sample
Unlike the analog days of statistics, when you might have given a survey to 1,100 people to stand in for your entire customer base, computing power today means you can look at all the data. And using all the data instead of a sample can make an enormous difference.
3. โDataficationโ
Viktor Mayer-Schรถnberger and Kenneth Cukier coined the term โdatafication,โ meaning that inexpensive sensors, hardware, and data storage have made it possible to collect certain types of data that were impractical to track previously.
4. Data exhaust
Because storage and collection has gotten cheap, you can save the equivalent of data โjunkโ and perhaps find ways to use it. For example, Google receives a large amount of search queries with typos or misspelled words each day. The company has taken this โexhaustโ from its lucrative search engine business in order to not only improve search (โDid you mean ornithologist?โ) but also to build a powerful spell-checker.
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