We develop robust, monitored and versioned data pipelines for companies that need to move, transform and centralize their data reliably and automatically. From batch ETL/ELT pipelines with Python, SQL and dbt to real-time streaming architectures with Kafka and Spark, we build the data infrastructure you need to feed your analytics, dashboards and AI models.
Data Pipeline Development for Companies
At MiT Software we develop custom data pipelines for companies that need to automate the movement and transformation of their data between systems. A well-built data pipeline is the difference between an organization that makes decisions based on updated and reliable data, and one that spends time and resources on manual error-prone processes. Our pipelines are developed following DataOps practices: Git versioning, automated data quality testing, data model documentation and real-time monitoring with alerts. We work with Python, SQL, dbt, Apache Airflow, Prefect, Apache Kafka, Apache Spark and all the tools of the modern data ecosystem.
We start with a detailed analysis of all data sources involved: structure, volume, update frequency, data quality, access restrictions and transformation logic needed to adapt the data to the target model. This analysis defines the pipeline architecture and the implementation order.
We define the complete architecture of the data pipelines: how many layers to implement (raw, staging, marts), what transformation logic to apply in each layer, what tools to use for each type of transformation and how to structure the data model to optimize analytical performance.
We develop all pipelines following DataOps practices: code in Git with pull request review, automated unit and integration tests, CI/CD deployment pipeline, staging environment for validation before production and complete documentation of each pipeline's behavior.
We configure the orchestration platform (Airflow, Prefect or the equivalent) with all the pipelines, their schedules, dependencies and retry policies. We configure the execution environments — cloud functions, containers, dedicated clusters — according to the resource requirements of each type of pipeline.
Before switching to normal operation, we execute the initial load of historical data with exhaustive validation: record counting, checksum comparison and statistical testing of key metrics to confirm that all historical data has been migrated correctly and completely.
After deployment, we provide continuous support for the operation and evolution of the pipelines: incident resolution, optimization of slow pipelines, incorporation of new data sources, adaptation to structural changes in the sources and evolution of the data model as business needs evolve.
Manual processes for moving and transforming data between systems are slow, error-prone and impossible to scale. A well-built data pipeline automates those processes completely: data moves, transforms and arrives where it needs to be reliably, on schedule and with automatic alerts when something fails.
The value of data is directly proportional to its freshness. We build pipelines that keep your analytics platform fed with updated data in the required frequency — from daily batch to near real-time — so that every dashboard, report and AI model always reflects the current reality of the business.
We develop custom extraction, transformation and loading pipelines with Python and SQL, adapted to the specific requirements of each data source and destination. Whether batch, micro-batch or streaming, we build pipelines optimized for the volume, latency and transformation complexity of each use case.
dbt is the standard tool for managing SQL transformations in modern data warehouses. We implement dbt to define, document, version and test all data transformations in your platform, applying software engineering practices — Git, CI/CD, unit testing — to data transformation code.
We implement and operate pipeline orchestration platforms that coordinate the execution of all data workflows: dependency management between tasks, automatic retries on failure, execution scheduling, centralized monitoring and alerting so your team always knows the state of the pipelines.
For use cases that require processing data in real time — fraud detection, live monitoring, personalization, event streaming — we build streaming architectures with Apache Kafka as the messaging backbone and Apache Spark Streaming or Apache Flink as the real-time processing engine.
We develop custom connectors and integrations for any data source that exists in your organization: relational databases, NoSQL systems, REST and GraphQL APIs, FTP and SFTP files, SaaS platforms like Salesforce, HubSpot or SAP, IoT streams or any other source with its own specific protocol.
We implement complete observability for your data pipelines: execution dashboards with processing times and volumes, automatic data quality alerts that detect anomalies before they reach the end users, structured logging for root cause analysis and SLA tracking for the most critical pipelines.
Tell us your challenge and get help for your next moves in 24 hours
Do you have any questions or concerns? If you would like to contact us, we are always here to help.click here and we will be glad to asssist you