Data engineering in data management ensures that data is seamlessly integrated, processed and made accessible for valuable analyses and business insights.
Data engineering in data management ensures that data is seamlessly integrated, processed and made accessible for valuable analyses and business insights.
Data engineering is at the core of any data-driven business and involves the design, development and optimization of data pipelines that efficiently collect, transform, store and deliver data. It forms the basis for analytics, machine learning and business intelligence by ensuring that data is of high quality, consistent and available in real time.
synvert helps companies to build robust and scalable data infrastructures that can be operated on-premises, hybrid or in the cloud. With a focus on modern technologies, best practices and the integration of standard tools such as Apache Kafka, Airflow, Spark, Snowflake or dbt, high-performance and flexible data solutions are developed. The aim is to optimize data processing, reduce operating costs and enable data-driven innovation.
The individual building blocks of Data Engineering cover all key areas of Data Processing – from integration and transformation to storage and monitoring.
Data Integration and Extraction – development of ETL and ELT pipelines that seamlessly integrate data from internal and external sources. Typical tools: Talend, Fivetran, Informatica, AWS Glue.
Stream processing and real-time Data Processing – development of streaming solutions for real-time data analysis and event-based systems. Typical tools: Apache Kafka, Apache Flink, AWS Kinesis, Google Dataflow.
Data Processing and transformation – use of powerful frameworks to process raw data and prepare it for analysis or machine learning. Typical tools: Apache Spark, dbt, Azure Data Factory, Pandas.
Data Architecture and storage – design of data architectures (data lake, data warehouse, data mesh) and selection of suitable storage solutions. Typical tools: Snowflake, Google BigQuery, AWS S3, Azure Synapse Analytics, Delta Lake.
Monitoring and quality assurance – implementation of systems to monitor data quality and pipeline performance. Typical tools: Great Expectations, Monte Carlo, Datadog, Prometheus.
Orchestration and automation – set up automated workflows to efficiently orchestrate and monitor data pipelines. Typical tools: Apache Airflow, Prefect, Luigi, Dagster.
Thanks to our comprehensive expertise and state-of-the-art technologies, synvert guarantees high-performance, future-proof and cost-efficient data engineering solutions. The benefits range from flexible scalability to ensuring high Data Quality.
synvert develops data pipelines that are flexible and robust to cope with growing data volumes and changing requirements – regardless of whether batch or real-time processing is involved.
With a broad technology portfolio, synvert selects the optimal tools for the use case, e.g. Spark for big data transformations or Kafka for event streaming.
The use of monitoring and testing tools such as Great Expectations or Datadog ensures the consistency and reliability of the data – a decisive factor for analyses and decisions.
synvert integrates state-of-the-art cloud technologies to build flexible and cost-effective data platforms that work with AWS, Azure or Google Cloud. These platforms can be easily scaled and seamlessly expanded.
The development of modular architectures that can be flexibly adapted to new technologies, data sources and business requirements guarantees long-term investment security.
synvert accompanies you through the entire process – from requirements analysis and tool selection to pipeline development, monitoring and continuous optimization.
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