Talend ETL Transformation Assistance

@codeandcommand

Talend jobs with SQL Server source and downstream table targets usually break at the schema mapping or job scheduling step, not the raw data move itself. I can jump into your existing workflow, find where it's failing or slowing down, and fix it. Can start right now, most fixes like this take 1 to 2 days once I see the actual jobs. The budget and timeline here are rough estimates from the post, I'll confirm real numbers once I look at your Talend setup. Want to share the job export so I can take a quick look?

@Mou192

I can optimize your Talend ETL transformation layer with a strong focus on data cleaning, mapping, and enrichment directly within Talend components. I’ll handle deduplication, malformed values, date/numeric standardization, schema mapping, lookups, SCD logic, reference-data enrichment, calculated fields, and metadata while keeping extraction and loading stable. I’ll deliver clearly commented, TAC-schedulable jobs and walk you through any custom routin

@JoseRCarvas

Hi — I'll fix and extend your Talend to SQL Server ETL: diagnose the transformation, correct the mapping/flow into the downstream tables, and validate row counts end-to-end so the data lands clean. I hold a 5.0-star rating here and deliver clean, tested, working code. We can start with a small funded milestone so you see real progress before committing further. Ready to start today — happy to talk through the details. - Ricardo

@alfatrade2

Standardising dates and numeric fields across SCD lookups inside Talend components is the part that usually breaks silently when source formats drift — do you already have the target schema mapped out, or is that still being defined alongside the transformation logic? I've built data pipelines handling cleaning, calculated fields and reference-data enrichment before load stages, so tightening this layer without touching your stable extract/load is straightforward. One flag: enrichment against external reference sets can add runtime if not indexed properly on the SQL Server side — worth checking before we scale it up.

@marvind18

Your extraction and loading already work; the Talend transformation jobs need cleaner values, accurate mappings, and enrichment before data reaches the target tables. At Marin Software, I worked on production data pipelines where validation and dependable downstream output mattered. I’d start by reviewing the existing jobs and target schema, then trace a sample of records through each transformation. I’ll address duplicates and malformed values, standardise dates and numbers, check lookup matches and data types, and verify how dimension changes should be handled. I’ll keep the changes inside Talend, document any routines or context variables, and walk you through the finished jobs so you can maintain and schedule them in TAC. How many Talend jobs and target tables are in scope for the first assignment?

@Wonitaf

Hi there, I totally get the importance of a clean and professional ETL transformation workflow, especially when it comes to data integrity. With my extensive experience in Talend, I’m confident I can help you tighten up those areas you mentioned. I noticed you need assistance with data cleaning, mapping, and enrichment within Talend. I’m all about making sure that your data is accurate and seamlessly integrated into your target tables. I’ll ensure we tackle duplicates, fix those malformed values, and get your schema aligned just right. I specialize in Talend and am skilled at creating automated workflows that are both user-friendly and efficient. I’ll also provide clear, well-commented artifacts and walk you through any custom routines we develop together. I’m the right person for this project because I prioritize speedy communication and a fast turnaround. Let me know if you are available for a quick chat! Regards, Wonita

@Tonifreelancer1

I can absolutely assist in tightening your data transformation workflow, focusing on meticulous data cleaning, robust data mapping, and intelligent data enrichment from your SQL Server sources. My Python-driven approach ensures your target tables receive precisely typed and formatted data, ready for downstream use. My process would involve three clear steps: * **Targeted Extraction:** I'll set up a custom Python script to pull the raw data directly from your SQL Server, capturing all necessary fields efficiently. * **Data Validation & Cleaning:** My Python automation will meticulously handle deduplication, fix malformed values, standardize dates/numerics, and perform active verification against reference datasets. * **Deliverable Format:** You'll receive perfectly clean, ready-to-load Excel or CSV files, structured precisely to your new schema with all enriched data and meta-information. I'd be happy to provide a free 10-15 row sample batch of your transformed data upfront, processed via my Python methods, so you can inspect the quality with zero risk. Let's get your data flowing flawlessly.

@mikhailj89

Hi, I’m Mikhail. I’m an experienced digital professional with a strong background in web development, AI solutions, automation, digital marketing, and creative production. I’ve reviewed your project, and I understand that you need focused help on the transformation layer of your ETL workflow in Talend. My expertise in data cleaning, mapping, and enrichment aligns perfectly with your requirements. My approach is simple: I will assess your current workflow, implement robust solutions for data cleaning to remove duplicates and standardize values, and build efficient data mappings to ensure your target tables receive the correct formats. Additionally, I will enrich your data with reference datasets and calculated fields. I will provide clear, well-commented Talend artifacts, and I’m committed to walking you through any custom routines or context-variable tricks I introduce, ensuring you can maintain them after our collaboration. I’d be happy to discuss your requirements further and show you relevant examples of my work.

@joeyb05

Not happy with the final result? Don’t pay. Your satisfaction comes first. I recently helped a client turn their idea into a clean, professional, and user-friendly solution, and I’d love to do the same for you. With extensive experience in Talend, I can help you enhance your ETL workflow by focusing on data cleaning, mapping, and enrichment. I will ensure that duplicates are removed, malformed values are fixed, and your data aligns perfectly with the new schema. Additionally, I’ll incorporate reference datasets and calculated fields to enrich your data effectively. I prioritize quality, usability, and the small details that make a project stand out. I'm happy to make adjustments until you’re completely satisfied and will provide clear, well-commented Talend artifacts that you can easily schedule in TAC. I’ll also guide you through any custom routines or context-variable tricks for seamless maintenance. Send me a message and let’s discuss your project. Regards, Joey

@B1zt

Hi, Your extraction and loading already work, so what you need is a transformation layer you can trust and keep running yourself after hand-off. I would start with cleaning, because mapping and enrichment are only as good as the data coming into them. What I would do first: - Read through your current Talend job and check the source data in SSMS to see where the bad values come from - Remove duplicates and fix malformed values inside Talend components, not in separate SQL - Standardise dates and numeric fields so every row arrives in one format - Write any custom routine in plain Java with clear comments, so it is easy to follow later - Walk you through the changes on a call, so you can schedule and maintain the job in TAC USD 150 for the cleaning stage, done within a week. Mapping, slowly changing dimensions (keeping history when a record changes) and enrichment would come next as a second stage. I would price that once I have seen the job. One question: which target tables need to keep history when a value changes, and which can simply be overwritten? Would enjoy getting this layer tidy and easy for you to own. Thanks, Bhagya

@amannamdev5081

The main focus here should be keeping the transformation logic inside Talend while making the existing SQL Server extraction/loading flow untouched and maintainable. I can work through the transformation layer for data cleaning, schema mapping and enrichment—handling duplicate records, malformed values, date/numeric standardisation, lookups, calculated fields and target data types. For slowly changing dimensions, I’ll structure the transformation logic around the required source-to-target behavior rather than pushing the processing back into SQL Server. SSMS can be used for validation and source/target checks, while the transformation logic remains within the Talend jobs as requested. I’ll also keep the jobs organized and commented so they can be scheduled through TAC without unnecessary custom complexity. Any context variables or custom routines introduced will be documented and explained during handover, including where they affect the transformation flow. Relevant experience: • SQL and relational database processing • ETL / data integration workflows • Data cleaning and transformation • Schema mapping and validation • Database-driven applications I’ll focus on producing maintainable transformation jobs rather than one-off data fixes.

@ronakkataria1

Since extraction and loading are stable, the useful place to start is what your tMap is doing with lookups. By default a tMap lookup loads the whole reference into memory before the main flow runs. That is fine at small volumes and turns into the thing that breaks your job when a dimension grows, so for the bigger lookups it is worth switching to reload at each row with a parameterised query, or pushing the join to the database where the index already exists. Slowly changing dimensions are the other place Talend surprises people. tMap alone will not do Type 2. You need the change detection, the close-out of the current row and the insert of the new version, either with the SCD component pointed at the right surrogate key and effective dates, or as a database-side merge if the target supports it. Which one is right depends on how your target handles surrogate keys. On dedup, tUniqRow keeps the first row it sees on the key columns you pick, so the ordering upstream decides which duplicate survives. If the newest record should win, sort first or dedup in SQL. Which database is the target, and is the dimension Type 1 or Type 2 today? Ronak

@JustinBee85

I understand you're looking for focused assistance with your Talend ETL transformation layer. With my extensive experience in full-stack development and a strong background in data integration, I can help enhance your workflow effectively. I specialize in data cleaning, ensuring duplicates are removed and values are standardized directly within Talend. I can also streamline your data mapping to align source columns with the new schema, addressing slowly changing dimensions to ensure accurate data formatting. Additionally, I’ll integrate reference datasets and calculated fields to enrich your data before it reaches the target tables. I will provide clear, well-commented Talend artifacts for scheduling in TAC, and I’m committed to walking you through any custom routines we implement. Let's discuss how we can optimize your ETL process together.

@RajarshiHalder

Hello, I can help improve the transformation stage of your SQL Server–Talend ETL workflow. I have 25+ years of experience with enterprise data platforms, including SQL Server, ETL design, data modelling, data standardisation, and delivery handover. I would start by reviewing the existing Talend jobs and target schema, then work through the three areas you identified: cleaning and standardising source values, mapping columns and lookups to the new schema, and enriching records with reference data, calculated fields, and metadata. I would also check duplicate handling, data types, and slowly changing dimension rules against sample records and target results. I understand that the transformation work needs to run in Talend, with SSMS used for checks. I’ll provide clearly documented jobs suitable for your TAC scheduling process and walk you through any routines or context variables introduced. My CV demonstrates the data engineering and SQL background for this work, though it does not list Talend as a current hands-on tool. I’d be glad to review a sample job and discuss the Talend version and transformation rules before confirming the implementation scope. Regards, Rajarshi Halder

@RevS412

The transformation layer should be corrected inside the Talend job rather than patched downstream in SQL; otherwise duplicates, malformed values, and schema mismatches will return on future loads. I’ll review the current job and sample source/target rows, then tighten the cleaning, type conversion, mapping, lookup, and enrichment components. I’ll validate the output against SQL Server, keep the Talend artefacts readable, and document any custom routine or context variable used. I’ll start from one representative failing transformation case, confirm the expected target schema, then apply the same rules across the job.