--- title: "Data integration and analytics" description: "Ingestion, transformation, warehousing, streaming and governance, so the numbers in every report trace back to their source." url: https://www.expandware.com/services/integration/data-integration-and-analytics/ section: "Services" topics: ["Integration", "dbt", "Apache Airflow", "Dagster", "Snowflake"] publisher: "Expandware Private Limited" --- # Data integration and analytics Ingestion, transformation, warehousing, streaming and governance, so the numbers in every report trace back to their source. Data infrastructure your decisions can stand on. Dashboards are only as honest as the pipelines behind them. We build the layer underneath: ingestion, transformation, warehousing and streaming, with the quality and lineage controls that make the numbers defensible in a board meeting. The same platform feeds AI systems, which is why it sits with integration rather than apart from it. Where it usually starts: - Two systems report different numbers for the same metric and nobody owns the definition. - Reports are built from exports that someone refreshes by hand. - An AI initiative is waiting on data nobody can find or trust. - Reporting queries are slowing down the production database. What is included: - Ingestion: Batch and change data capture from databases, SaaS platforms, files and APIs. - Transformation: Modeled, tested and versioned transformations with dbt or the tools you already use. - Warehouse and lakehouse: Snowflake, BigQuery, Redshift, Databricks or PostgreSQL, chosen to the workload. - Streaming: Kafka and cloud streaming for data that has to arrive in seconds. - Quality and lineage: Tests, freshness checks and lineage from every report back to its source. - Analytics enablement: Semantic models and dashboards in Power BI, Looker or your BI tool. Where it sits: This service owns the data path through the integration architecture: pipelines that draw from legacy databases and business systems, and the warehouse they land in, with the quality checks and lineage that make every downstream figure traceable. Decisions to settle early: - Warehouse, lakehouse or your database. A dedicated warehouse suits shared analytics at scale; a lakehouse suits mixed data and machine learning; a read replica can be enough for modest reporting. We choose to the workload. - Batch or streaming. Most reporting is fine with data that is minutes or hours old. Streaming earns its complexity where decisions depend on seconds. - Central team or embedded analysts. A central data team keeps definitions consistent; embedded analysts know the business. Most organizations need a small central core that the others build on. How the work runs: Ingest, Sources, legacy systems and spreadsheets. Transform, Modeled, tested and versioned. Govern, Quality, lineage and an owner per metric. Serve, Reports, APIs and AI retrieval. How it is bought: - A fixed-fee assessment: We inventory your sources, trace a handful of disputed metrics back to where they come from, and review the current reporting stack. You receive the findings, the target design and a fixed price for building it. - A fixed-price build: Ingestion, tested transformations, the warehouse or lakehouse, quality monitoring and the first governed reports, delivered at a fixed price for the sources and metrics named in the scope. - Running it afterwards: Your analysts can take over the models and pipelines with documentation, or we run the platform monthly, covering new sources, failed loads and changes to definitions. Worked example, two numbers for one metric. Say a subscription business reports revenue from its billing platform and from its finance system, and the two never agree. Board packs are assembled from exports, and only one analyst knows how. - Define: We agree a single definition of each disputed metric with the people accountable for it, and write down why the two systems differ. - Ingest: Billing, finance and CRM data are loaded into a warehouse through change data capture, so nobody exports anything by hand. - Model: Transformations implement the agreed definitions as tested code, and lineage records which source fields feed each figure. - Serve: Board reporting is rebuilt on the governed models, with quality checks that hold a report back when its data is stale. At the end, revenue has one definition and one number, any figure in the board pack can be traced to its source, and producing the pack no longer depends on one person. (An illustration, not a client account.) What you have at the end: - Source inventory and data contracts: Every source system, what it provides and how often, with a contract that states what the pipeline can rely on. - Tested transformation code in your repository: Transformations as versioned code with tests, so a change to a metric is reviewed like any other change. - Lineage documentation from report to source: For each report, the tables, transformations and source fields behind every number on it. - Data quality monitoring and alerting: Freshness, volume and validity checks on the data that matters, with alerts before anyone opens a stale report. - Metric definitions with named owners: One written definition for each business metric, with the person accountable for it. Standards and platforms worked with: dbt, Apache Airflow, Dagster, Snowflake, BigQuery, Amazon Redshift, Databricks, Apache Kafka, Power BI, Looker. Where it goes wrong, and what we design out: - Dashboards before definitions. A dashboard built on an undefined metric spreads the disagreement faster. Each disputed number gets one written definition and an owner before it is modeled. - Logic hidden in reports. Calculations written into individual reports drift apart. Business logic lives in tested, versioned models that every report reads from. - Silent pipeline failures. A load that fails overnight produces confident wrong numbers by morning. Freshness and volume checks hold a stale report back and alert its owner. What it changes: - Trusted data contracts. Sources, schemas, ownership and freshness expectations are explicit, so downstream users are not left guessing what changed. - Pipelines that can be operated. Orchestration, testing, alerting and replay paths give your team an answer when data is late, incomplete or wrong. - Metrics leaders can defend. Business definitions are modeled with the data, linking every report to its source logic. How you will know it is working: - Two reports using the same metric always show the same figure. - Any reported figure can be followed back to the fields it came from. - A failed load is known to its owner before the report is opened. - Analysts spend their time on questions rather than on exports. Who you work with: - Data architect. Designs the platform, the models and the governance, and owns the metric definitions with your business owners. - Analytics engineers. Build and test the pipelines and transformations, and document lineage from report to source. - BI developer. Rebuilds the priority reports on the governed models in your BI tool. What we need from you: - Read access to the source systems and their schemas. - Business owners who can agree what each disputed metric means. - The reports people rely on today, including the spreadsheets. - A decision on where the platform should be hosted. Common questions: Q: Our numbers disagree between systems. Can that be fixed? A: Usually, and the fix is rarely technical first. Two systems disagree because they define the metric differently, so the work starts with agreeing the definition and an owner for it, then enforcing that definition in the pipeline. Q: Do we need a warehouse, or is our database enough? A: Frequently your database is enough, and we will say so. A warehouse earns its cost when reporting load threatens production, when you join across sources, or when you need history the operational system does not keep. Q: How do you handle personal data in a pipeline? A: Classification first, then minimization: fields that do not need to travel do not travel. Where data must move, we design for residency, retention and the ability to delete a record everywhere it landed. Q: Can you prepare our data for AI? A: Yes. Retrieval systems need what reports need, clean sources, clear ownership and access controls, plus document chunking, embeddings and permissions that follow the user. We build both from one platform so they cannot drift apart. Q: What is change data capture? A: A way of copying data out of an operational database by reading its change log rather than querying its tables. The pipeline receives each insert, update and delete as it happens, without adding load to the production database or waiting for a nightly export. Q: Who should own a metric definition? A: The business function accountable for the number, not the data team. Finance owns revenue; operations owns fulfilment times. The data team implements the definition and keeps it tested, and the owner decides what it means and approves any change to it. Q: Can you build on the BI tool we already use? A: Usually, yes. Power BI, Looker and most other tools work well on a governed warehouse. What changes is where the logic lives: calculations move out of individual reports into tested models, so every report that uses a metric uses the same one. Q: How do you stop a pipeline failing silently? A: Each critical dataset has checks for freshness, volume and validity, and a failed check alerts its owner and marks the affected reports as stale. A dashboard showing yesterday's numbers as today's is treated as an incident, not a curiosity. Often paired with: Generative AI and agents, Application and API integration, Managed AWS, Azure and Google Cloud. --- Canonical page: https://www.expandware.com/services/integration/data-integration-and-analytics/ Site index for machines: https://www.expandware.com/llms.txt Full site text: https://www.expandware.com/llms-full.txt Expandware Private Limited. Inquiries: solutions@expandware.com, +92 (333) 32 11011.