AI Use Cases
synvert Accelerators
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Scalable data analysis gives companies a competitive advantage by enabling fast, data-based decisions. An analytical data lake combines structured and unstructured data from various sources and enables powerful analyses of large volumes of data.
Scalable data analysis gives companies a competitive advantage by enabling fast, data-based decisions. An analytical data lake combines structured and unstructured data from various sources and enables powerful analyses of large volumes of data.
The analytical data lake forms the basis for future-proof, scalable data analyses. By integrating structured and unstructured data from sources such as databases, IoT devices and social media, comprehensive analyses can be carried out. Technologies such as Apache Hadoop, Apache Spark, Presto and Delta Lake ensure flexibility, automation and scalability. Our focus is on real-time data analytics with Apache Kafka, self-service analytics and ensuring data quality through data governance models.
synvert supports companies in implementing customized analytical data lakes that are tailored to their requirements and use cases. The goal is a powerful data platform that accelerates data-driven decisions and secures long-term competitive advantages.
synvert integrates the analytical data lake seamlessly into your data strategy. From data import, storage and analysis to ensuring data quality, the entire data lifecycle is covered. Through self-service analysis and real-time data processing, we offer a flexible, scalable strategy that enables data-driven decisions and meets both current and future requirements.
Scalable Data Architecture – development of a robust analytical data lake with Amazon S3, Google Cloud Storage or Microsoft Azure Data Lake to centralize and analyze structured and unstructured data.
Data integration and preparation ‑iIntegration of heterogeneous data sources (SQL, NoSQL, CSV, JSON) for in-depth analyses in the data lake.
Self-service analytics – providing a user-friendly platform for data-driven decisions with tools such as Tableau, Power BI or Looker.
Automation and orchestration – implementation of automated pipelines for data integration with Apache NiFi, data cleansing and transformation with Apache Airflow or dbt.
Modern analysis tools – integration of advanced tools for machine learning (TensorFlow, PyTorch) and artificial intelligence (Google AI Platform, AWS SageMaker).
Data Quality – ensuring data quality through automated tests and continuous monitoring.
synvert develops scalable analytical data lake architectures that respond to future requirements and can be extended with technologies such as Apache Hadoop and Delta Lake.
Ensuring data quality through automated pipelines and data governance models for continuous monitoring and ensuring data standards.
Integration of the analytical data lake into existing environments – on-premises, hybrid or in the cloud (e.g. Amazon S3, Azure Data Lake).
Implementation of streaming functions with technologies such as Apache Kafka for real-time data analysis and integration.
Covering the entire data lifecycle from extraction to final analysis for an end-to-end solution.
Provision of platforms that enable self-service analysis with tools such as Tableau and Power BI.
Analytical data lakes, which offer high scalability thanks to flexible cloud solutions to keep pace with growing data volumes and changing business requirements.
Use of advanced analysis tools and AI technologies such as TensorFlow and AWS SageMaker to gain deeper insights from the data and integrate machine learning models directly into the data lake.
With flexible cloud solutions such as Amazon S3, Google Cloud Storage or Microsoft Azure Data Lake, companies only pay for the capacity they actually use and can scale as required.
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