Data Quality Management Tools

Data cleansing is the most manually intensive activity in data management. Only 14% of organizations have implemented operational tools that automate data quality management processes like profiling, matching, correction, and enhancement. The rest rely on data stewards to close the loop manually. On the other hand, data quality issues detection tools have gotten dramatically better over the past decade: ML-based anomaly detection, automated profiling, and real-time monitoring. But every alert still ends the same way. A steward exports the bad records, chases the source system owner, fixes the data in a spreadsheet, and reimports. Organizations operationalized detection but not remediation. Agentic data cleansing changes that. It uses specialized AI agents that detect failures, analyze the source to find what "correct" looks like, propose a targeted fix, and wait for a human to accept. The steward governs. The agent does the janitorial work. This latest guide covers three generations of data cleansing (manual scripts, rule-based automation, and agentic cleansing), an evaluation framework for deciding when your team needs agentic cleansing, a market landscape comparison, and the architectural insight that makes contract-driven remediation possible. Whether you're a data steward drowning in alert fatigue or a governance leader trying to operationalize quality beyond observability, this is the resource you'll come back to. Read here: https://lnkd.in/d-ceTJzU

𝗧𝗵𝗲 𝗱𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗹𝗼𝗼𝗸𝗲𝗱 𝗳𝗶𝗻𝗲. 𝗧𝗵𝗲 𝗻𝘂𝗺𝗯𝗲𝗿𝘀 𝘄𝗲𝗿𝗲 𝘄𝗿𝗼𝗻𝗴 𝗳𝗼𝗿 𝘁𝗵𝗿𝗲𝗲 𝘄𝗲𝗲𝗸𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗮𝗻𝘆𝗼𝗻𝗲 𝗻𝗼𝘁𝗶𝗰𝗲𝗱. Ep 42 covered monitoring: how you detect problems.  This episode covers how you prevent them from reaching production in the first place. Data quality as code means embedding validation checks directly into your pipeline, not running them after something breaks. 𝗪𝗵𝗮𝘁 𝗺𝗼𝘀𝘁 𝘁𝗲𝗮𝗺𝘀 𝗱𝗼: → Spot-check data manually after a stakeholder complains. → Write one-off SQL queries to investigate. → Fix the issue. Move on. Same problem returns next quarter. 𝗪𝗵𝗮𝘁 "𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝘀 𝗰𝗼𝗱𝗲" 𝗺𝗲𝗮𝗻𝘀: → Assertions in the pipeline. "Order amount is never negative." "Row count within 10% of yesterday." "No duplicate primary keys." These run automatically, every time. → Tests at layer boundaries. Validate at ingestion (is the source clean?), after transformation (did the logic produce expected results?), and before serving (is this safe for consumers?). → Version-controlled checks. Quality rules live in the same repo as pipeline code. They go through PR review. They have history. They evolve with the data. → Fail-fast behavior. When a check fails, the pipeline stops. It is better to deliver a late report than a wrong one. 𝗧𝗼𝗼𝗹𝘀 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝘁𝗵𝗶𝘀 𝗽𝗮𝘁𝘁𝗲𝗿𝗻: → dbt tests: built-in assertions (unique, not_null, accepted_values, relationships) plus custom SQL tests. → Great Expectations: expectation suites with profiling, data docs, and orchestrator integration. → Soda: lightweight checks defined in YAML, designed for pipeline integration. If your only test is eyeballing dashboards, you don't have data quality. You have luck. What quality check would have caught your last data incident earliest? #DataEngineering #DataQuality #DataPipelines

 ""Banking Data Pipeline with Kafka + Glue + IDQ + Control-M"" =>In banking, data pipelines are not just about moving data. They’re about protecting trust, detecting fraud, and ensuring compliance. =>This Banking ETL Pipeline (Kafka + AWS Glue + Informatica IDQ + Control-M) ensures validated, high-quality data flows seamlessly from raw ingestion to business dashboards (Power BI / Tableau / Looker): |Pipeline Flow| >>Ingestion (Kafka) – Streams real-time transactions from ATMs, mobile apps, and payment systems. Fraud detection systems connect here to flag anomalies instantly. >>Batch ETL (AWS Glue + Amazon S3) – Transforms, cleanses, and lands data into S3 for further processing. >>Data Quality (Informatica IDQ) – Applies rules for completeness, deduplication, reconciliation, and compliance validation before downstream use. >> Orchestration (Control-M) – Automates workflows, manages dependencies, and ensures SLAs across the pipeline. >> Consumption (BI Tools: Power BI, Tableau, Looker) – Delivers trusted, business-ready data for reporting, compliance dashboards, and advanced analytics. >> Business Impact =)Fraud detection latency reduced from minutes → seconds =) 30% boost in data quality with IDQ validation gates =) 40% less manual intervention via Control-M orchestration =) Regulatory reporting accelerated with traceable, validated datasets #Banking #DataEngineering #Datamodeler #Dataquality #Ingestion #IDQ #S3 #Kafka #AWSGlue #Informatica #ControlM #PowerBI #Tableau #Looker #ETL #DataQuality #FraudDetection #Fintech #Compliance #BigData #C2C #C2H #Opentowork #USITRecruiters

🚀 Exciting news for Databricks Data Quality enthusiasts! 🌟 Databricks has introduced DQX (Data Quality Check), a powerful tool designed to ensure the integrity of your data in real-time! 📊✨ With DQX, you can easily implement data quality checks on your PySpark workloads, both in streaming and batch processing. This means you can catch data quality issues as they happen, rather than waiting for post-processing checks. 🕒🔍 Why is DQX Useful? 🤔 • Detailed Insights: DQX provides comprehensive explanations for any data quality failures, allowing you to quickly identify and resolve issues. 📉🔧 • Quarantine Invalid Data: It enables you to quarantine bad data, ensuring that only clean, validated data flows into your analytics pipelines. 🚫📦 • Integration with Medallion Architecture: In a medallion architecture, DQX plays a crucial role by validating data at the entry point into the Curated Layer. This prevents the propagation of bad data through your system, maintaining high-quality datasets throughout. 🏗️🔗 How DQX Works 🛠️ DQX simplifies the process of validating data quality by providing a Python validation framework tailored for PySpark DataFrames. This framework allows real-time quality validation during data processing, which is essential for both streaming and batch workloads. The validation output includes detailed information on why specific rows or columns have issues, enabling quicker identification and resolution of data quality problems. Possible Use Cases 💡 • Data Governance: By implementing DQX checks, businesses can establish robust data governance frameworks that prioritize data quality and compliance across all layers of their architecture. 📜✅ • Streaming Data Quality Checks: For organizations leveraging real-time analytics, DQX can validate incoming streams of data instantly, ensuring only valid records are processed. 📈💬 • Batch Processing: When working with historical datasets, DQX can assess and clean your data before it enters the analytics phase, enhancing the reliability of insights derived from it. 🗃️🔍 • Quarantine Management: DQX allows organizations to manage quarantined invalid records effectively. After thorough examination and correction, these records can be re-ingested into the pipeline to ensure that all datasets meet established quality standards. In conclusion, DQX is not just a tool; it's an essential component for any organization aiming to uphold high standards of data quality in their analytics processes! 🖥️🔗 #Databricks #DataQuality #DQX #PySpark #MedallionArchitecture #DataGovernance #Batch #Streaming

Discover → Control → Trust → Scale Governance is not a tool. It’s a layered system: Catalog – discover, tag, and connect data + AI assets. Quality – enforce correctness, freshness, and reliability. Policy – codify who can do what, where, and how. AI Control – govern models, prompts, and usage. Break one layer → trust breaks. Good governance doesn’t slow data down — it makes it usable, trusted, and AI-ready. With so many tools out there, the real question is simple: what helps your team trust data faster? Here's the breakdown to adapt and integrate with Data Governance: ⚙️ 1. ENTERPRISE GOVERNANCE TOOLS Collibra – Enterprise‑grade governance platform for glossary, lineage, and policy‑driven stewardship. Atlan – AI‑powered data catalog that enables self‑service discovery and governance‑as‑code. Informatica Axon – Unified governance hub for policies, lineage, and MDM‑integrated data. Alation – AI‑driven catalog and search engine built for analyst‑centric discovery. OvalEdge – Governance and compliance platform focused on sensitive‑data detection and templates. Secoda – Lightweight AI catalog for modern data teams with simple issue tracking. ☁️ 2. CLOUD‑NATIVE GOVERNANCE Databricks Unity Catalog – Single governance layer for data and ML across the Databricks lakehouse. Google Cloud Dataplex – Unified data governance and profiling layer for GCP data lakes. Microsoft Purview – Cross‑Azure catalog, classification, and sensitivity‑label governance engine. Snowflake Horizon – Native governance and access control layer built into Snowflake. Google Cloud Data Catalog – Metadata discovery and integration layer for BigQuery and Vertex AI. 🔄 3. PIPELINE + QUALITY LAYER dbt Labs – Transformation‑forward framework that enforces data contracts and testing in pipelines. Great Expectations – Validation framework that codifies data quality expectations and tests. Soda – Observability tool for monitoring data freshness, distribution, and anomalies. ⚡How to decide, where to begin with? Single platform  → Start with Unity Catalog / Dataplex / Purview / Snowflake Horizon. Multi‑cloud  → Add Atlan / Collibra as cross‑platform governance. Data quality issues  → Enforce contracts with dbt + Great Expectations. The smartest governance stacks don’t rely on one tool, Instead they combine catalog, quality, lineage, and policy where each matters most. #data #engineering #AI #governance

Data quality shouldn't be a part-time job for your best engineers. For the longest time, data quality meant manual rules, endless alerts, and pure firefighting. Something would break → An alert would fire → We’d dive into lineage, logs, and chaotic Slack threads → Hours (or days) later, we’d finally find the root cause. By then? The dashboards were already wrong. 📉 The AI models? Already poisoned. I realized something recently: data quality can’t be reactive anymore. It has to be autonomous. This is why Acceldata’s Data Quality Agent caught my attention. It’s a shift from watching the house burn to a system that actually puts out the fire: 1. Continuous Monitoring: It scans pipelines and tables nonstop. 2. Contextual Diagnosis: It doesn't just say "it's broken." It uses lineage and the xLake Reasoning Engine to explain why it broke. 3. Proactive Remediation: It can auto-reprocess only the impacted data instead of a full, expensive rerun. 4. The HITL Balance: You aren't losing control. You still review anomalies and approve remediation 5. The bottom line: If you're still using manual rules and 16-day QA cycles, your AI will never be "production-ready." Move from fragile to fail-safe. 🚀 👉 See the Agent in action (and try the Free Trial): https://lnkd.in/dQsXyhaN

Data Quality has a large impact on an organization’s ability to effectively build Data & AI Strategies. But when we talk about Data Quality we aren’t just talking about performing a DataFrame.describe() to see whether your data fits into your expected format. Here are a few data quality checks that your data team should implement to accommodate upcoming AI related requests. 1. Data Pipeline Checks - This will test the shape of your data as it is arriving into your data pipeline. Note, we aren’t testing the structure of the pipeline itself here (testing whether files get landed in S3 or not), but rather testing to see whether the data ingested into your pipelines follow an expected standardized schema Tools that I very much enjoy using that specialize in Data Pipeline checks are Great Expectations and Spark Expectations. Great Expectations also has powerful Data Profiling features that enable your team to do data discovery 2. Data Modeling Testing - These quality checks should be performed to test your data modeling logic. Here we are testing your SQL modeling logic using defined test cases with sample input data and expected output data of your model These tests are useful and cost effective as they don't run on live data in your data warehouse Data teams that leverage data modeling tools like DBT can leverage DBT Unit Tests to perform Data Modeling Tests 3. Live Data Checks - These are Data Quality Checks that run after your data pipelines and data modeling have been performed and your dataset is live in your data warehouse. These data quality checks test the quality of your 'data assets' Cloud Data Warehouses like BigQuery offer capabilities to directly perform scans against your data warehouse DBT also offer tests to perform quality checks on live data known as DBT Data Tests. These quality checks can be expensive because you are running queries on live data. Find links to Great Expectations, Spark Expectations, DBT Unit Tests & DBT Data Tests in the comments