SQL Data Automation Workflow

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in MySQL, PostgreSQL, SQL, Python As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@OutsourceMan

Hi, CnEL India can build your end-to-end SQL data automation workflow with repeatable ETL/ELT jobs, scheduled execution and clear monitoring. Our methodology: * Review SQL instances, source tables, transformation rules and target store. * Configure secure PostgreSQL/MySQL connections with environment-based credentials. * Design a parameter-driven ETL/ELT pipeline using Python, SQL and suitable open-source orchestration. * Keep table lists, filters, schedules and load behaviour configurable without code changes. * Extract required data on scheduled triggers. * Transform and validate data into clean analytics-ready structures. * Load results into the selected target database/store for BI consumption. * Add incremental/full-load handling and basic failure recovery where required. * Implement structured logging for successful jobs, failures and execution details. * Add basic alerts for failed or incomplete runs. * Keep credentials and sensitive configuration outside the source code. * Test multiple databases, transformations, scheduling and failure scenarios. * Provide setup documentation, environment variables and architecture/data-flow diagram. * Conduct a screen-share handover covering execution, monitoring and configuration updates. We would first finalize the source-to-target data flow, then build and test the pipeline before scheduling production automation.

@neerajdhameliya

Hello, Your main goal is to have fresh, analytics-ready data available automatically without manually extracting, cleaning, or moving data between systems. I can build a reliable SQL data automation workflow that connects your PostgreSQL/MySQL instances, extracts the required tables on schedule, transforms the data, and loads it into an easily queryable target store for BI. I can use Python + SQL with Airflow, Prefect, or dbt, with parameter-driven tables, filters, and schedules, secure authentication, logging, error handling, alerts, and configurable log storage. I’ll also provide documentation, an architecture diagram, and a handover session. Happy to discuss your setup and suggest the right approach. Best, Niral

@serazummunirz

Hi There, Hope you are well. I specialize in end-to-end data automation solutions, connecting to SQL databases for extraction, transformation, and loading into an analytics-friendly format. My expertise includes setting up connections, creating repeatable pipelines in Python or SQL, parameter-driven jobs for flexibility, robust logging/alerting, and detailed documentation for easy maintenance. Let's discuss further. Thanks in advance for the opportunity to working with you. Best Regards, Serazum

@denisSolution

Hello, I'm Denis, a full-stack developer, and I have built SQL automation workflows that move data reliably from source databases into clean, query-ready formats without manual work. You need an end-to-end pipeline that extracts tables from PostgreSQL and MySQL on a schedule, transforms them into analytics-ready data, and deposits the results somewhere your BI tools can reach. The key is that it runs automatically and your team can adjust what gets loaded and how often without writing code. I would start by mapping your source tables and target schema with you, then build the pipeline using Python and SQL with parameter-driven configuration so you control table lists and load frequency from a settings file. I would add logging to a file or database you specify, set up basic alerting for failures, and test the full flow end-to-end before handing it off. Then we would do a screen share session where I walk you through running it, checking logs, and making updates yourself. The critical part is reliability. I would build in retry logic for database connections and timeouts so transient network issues do not break the run, and structure the transformations as idempotent SQL so the pipeline can be rerun safely if needed. The main risk is unclear data requirements, so I would confirm the exact transformations and target schema early to avoid rebuilds later. I can start working right away. Let's connect and discuss the details. Thanks, Denis.

@srinichal

You need fresh, clean data from your PostgreSQL and MySQL instances landing in one queryable store on a schedule. First table pulled, transformed and loaded by one command on day 2. Approach: 1. Config first: one YAML file lists connections (secrets in environment variables), tables, filters, incremental keys and frequency. Changing a job means editing config, not code. 2. Extract and load in Python with SQLAlchemy, incremental by updated-at or ID so reruns are safe. 3. Transformations as versioned SQL models in dbt, run inside the pipeline, with null, duplicate and row-count tests. 4. Prefect schedules runs with retries, writes logs to the location you choose and sends failure alerts by email or Slack. On your example request: my option chain monitor runs a scheduled collector into MySQL, with endpoints and fields set in a YAML config and a one-command installer. I also built a FastAPI service on async SQLAlchemy and PostgreSQL. Both repos are private; I can screen-share the code and the install flow. Timeline: first job running on day 2, all jobs scheduled with alerts on day 5, docs, diagram and hand-off call by day 7. Daily progress notes, 14-day bug-fix window. Questions: 1. What is the target store: a PostgreSQL warehouse or something else? 2. How many tables and rows per run, and do the source tables have an updated-at column? Next step: share one source table schema to start the day-2 job. Thanks, Srinichal

@onkarkulkarni8

Hi, I can help you with "SQL Data Automation Workflow" as per your given project description. We can discuss more in detail during a chat conversation when you are available. I've worked on many PHP projects in recent times. So I am confident on achieving your expected Goals. Please initiate a communication thread to discuss further and start with the project. ⭐ 5.0/5 from a recent client: "Good work delievered thank you for your patience" Final timeline and cost will be confirmed in chat after a complete understanding and documentation of the project expectations in detail.

@syphaxzen

Hey! I'm a Python developer experienced in automation, data processing, SQL and building ETL pipelines. I can handle the full workflow from connecting to PostgreSQL/MySQL databases, extracting and transforming the data, to loading it into the target store. I can also make the pipeline parameter-driven, with logging, error handling, scheduling and clear documentation so it can run without manual intervention. I'm comfortable doing this as a **fixed-price project rather than an hourly project**, with the scope and deliverables clearly defined upfront. I can also provide a brief example of similar automation work and walk you through the final workflow during hand-off. Regards, Syphax

@jayantkumar314

I can build the complete SQL-focused automation pipeline connecting your PostgreSQL and MySQL databases, handling scheduled extraction, transformation, validation, and loading into your analytics-ready target store. I’m experienced with SQL, databases, APIs, server automation, Docker, logging, and production deployments, and can make the workflow configuration-driven so tables, filters, and schedules can be changed easily. I’ll also provide clear setup documentation, environment configuration, process diagrams, monitoring/error logs, and a handover covering how to operate and maintain the pipeline.

@farrukhdeveloper

Hi, This is exactly the kind of work I’ve handled for years: pulling data from multiple systems, cleaning it, making it reliable, and leaving behind a pipeline that runs without babysitting. I’m a Software Architect with 18+ years of experience in Python, SQL, PostgreSQL, MySQL, ETL/ELT, data pipelines, Airflow-style orchestration, Spark, cloud platforms, and production automation. I can build: • Secure connections to multiple SQL sources • Parameter-driven extraction jobs • Python/SQL transformation layer • Scheduled ETL/ELT workflows • Logging, retries, alerts, and failure tracking • Analytics-ready target tables/files • Environment-based configuration • Clear README + architecture diagram • Handover session for monitoring and updates I’d keep table lists, filters, schedules, and destinations configurable so future changes don’t require code edits. My focus would be reliability first: one command or scheduled trigger, deterministic transforms, clean logs, and no spreadsheet cleanup afterward. I also provide post-delivery support if you need new sources, schema changes, or BI integrations later. Regards, Farrukh Naveed Anjum Software Architect | Data Engineering & Automation

@techriverr

Hi, I can build the scheduled SQL workflow to extract from your PostgreSQL and MySQL databases, transform the data, and deliver analytics-ready results with logging and documentation. I’m ready to get started. Do you have a preferred target store or orchestration tool?

@emanz03

Partial loads and source-schema changes are where scheduled ETL usually becomes unreliable. I’d keep extraction, transformation and loading separate, make tables/filters/frequency config-driven, and add idempotent loads, retries, validation and structured logs. I’ve built Python data workflows with repeatable database update and validation stages. For PostgreSQL/MySQL, I can implement this with Python + SQL and Prefect or Airflow, including env-based credentials, alerting and clear handoff docs. What target store should receive the final analytics-ready data?

@helgekrueger

Hi. I might have build similar things before. Usually the problem does not arise in the steps you describe, but due to not being able to connect to the SQL databases directly, e.g. if you have a web application, one usually sets it up in a way that one can only connect to the db from the server running the application. External connections are not accepted. Depending on your setup, I know how to work around this, e.g. ssh forwarding. If there are no connection issues, I can build something that queries databases depending on parameters, extract the result and exports everything in CSV. Additional transformations are possible. I can have an initial prototype ready in an hour. Do you have a concrete output format in mind? If yes, knowing it would make writing the prototype, and estimating an exact time requirement easier.

@SamirBanna

I am very interested in applying for your job since it seems to fit very will with my experience and skills. Regards SamirBanna

@LordAskar

Configuring parameter-driven extractors for your PostgreSQL and MySQL instances so table lists and load filters can be adjusted via a clean config file without touching code is a very practical setup. I can build this end-to-end pipeline in Python using modular scripts and automated scheduling. In past client work, I have handled PostgreSQL data extraction and formatting in JSON Response Formatting for Lambda, as well as MySQL database management in Image Database Creation with ChatGPT. For your pipeline, I will set up the jobs to read parameters from an external config, execute all transformations internally, write logs and alerts to your chosen path, and deliver complete documentation with a hand-off screen share. I work under pre-agreed milestones. Which destination database or BI tool will serve as your target analytics store? Ahmed

@AZTURK1

Connecting heterogeneous PostgreSQL and MySQL sources into an automated, parameter-driven ELT pipeline is standard work for me. I can build a clean, containerized pipeline that handles extraction, transformation, and target loading completely hands-free. ✅ Config-driven extraction layer for PostgreSQL and MySQL instances using secure connection pooling and isolated environment variables ✅ Modular Python and SQL transformation pipeline (orchestrated via Prefect or scheduled Dockerized workers) controlled via YAML config files for dynamic table lists and intervals ✅ Destination warehouse/reporting schema setup with structured file/syslog logging and instant failure notifications via webhook or email ✅ Pre-run schema validation and atomic transaction writes to prevent partial loads or data corruption in your reporting store ✅ Complete documentation, environment template, and a live screen-share walkthrough session ✅ 10+ years production experience | 400+ completed projects | 320+ five-star reviews ✅ Microsoft® Certified: MCSA | MCSE | MCT & cPanel Certified (CWSA-2) What target database or storage engine are you planning to load into for your BI layer, and what is your approximate row volume per run? My rate is 20 USD/hour and I can start immediately.

@naminghn

Hi there, I can help you build an end-to-end data automation solution that seamlessly connects to your SQL databases for reliable data extraction and transformation. To meet your requirements, I suggest using a Python-based ETL pipeline that leverages libraries like Pandas for data transformation and SQLAlchemy for database connections. This way, we can set up parameter-driven jobs, ensuring flexibility in table lists and filters without needing code changes. I'll implement logging and alerting within the pipeline to track errors and successes, storing logs in a specified location for easy access. Moreover, I’m committed to providing you with comprehensive documentation and a hand-off session on how to manage and monitor the workflow effectively. Looking forward to your response. Regards, Ali

@Azursol

Hello, I understand the need for an efficient end-to-end data automation solution connecting to SQL databases for seamless extraction, transformation, and loading. With over a decade of experience in Python, SQL, MySQL, PostgreSQL, and data warehousing, I am well-equipped to tackle this project. My approach involves setting up secure connections to multiple SQL instances, developing a robust ETL/ELT pipeline using Python or SQL scripts, implementing parameter-driven jobs for flexibility, and ensuring thorough documentation and training for seamless handover. By incorporating open-source tools like Airflow or dbt, I can create a streamlined workflow that meets all your requirements, including reliable data extraction, automated transformations, detailed logging, and user-friendly documentation. Let's discuss how I can tailor this solution to your specific needs. Looking forward to collaborating with you on this project. Best regards,

@mykhaylob4

✋ Hi there. I can build your SQL data automation workflow with scheduled ETL and clean analytics-ready output. ✔️ I have strong experience building SQL-centric data pipelines, having delivered similar ETL workflows using Python, Airflow, and dbt with PostgreSQL and MySQL. I will develop this by setting up secure connections and authentication for multiple SQL instances, building a parameter-driven pipeline so you can adjust table lists, filters, and load frequency without touching code, implementing transformations inside the pipeline, and adding logging and basic alerting stored where you specify. I'll finish with documentation covering setup, environment variables, and a process diagram, plus a hand-off session walking you through running, monitoring, and updating the workflow. Please click the 'Chat' button to start our valuable conversation. Looking forward to collaborating with you! Best regards, Mykhaylo