Big data. Cloud data. ML, AI training data, and personally-identifying data. Data is all around you. The world is data-centric. Data Love is the conference for Data Engineers, Data Scientists, and everyone who wants to dive into the data-driven world.
REGISTER This conference is not only for engineering professionals but also for people of science. No matter which category you fall into, you’ll get a broad overview of the field, including what data engineering is and what kind of work it entails. The Data Love conference is an excellent opportunity to contact aspiring researchers and enthusiastic engineers. After visiting our conference, you’ll have better answers to the following questions about the two similar but pretty different divisions:
- Wonder about new trends in the Data engineering world?
- Interested in what is happening in Data Science?
- Puzzled about how to meet both engineering and scientific requirements?
Master real, in-demand tech skills from home with Data Love!
Data Engineering is one of the most interesting and broad topics itself, and we’re not limited to any particular topic. We are not restricted in technologies, languages, and platforms, use this list as an example of what might be interesting for us!
- Data computational Models
- Data Quality
- Data Standards
- Data Modelling
- Data Visualization
- Data Lake and Data Catalog
- Low-code development
- Business analytics
- Business intelligence
- Data governance: Availability, Usability, Integrity, Security, Migration
- Data Infrastructure: Logging and Tracing, Cloud services, Private cloud, BigData as a service, Data-intensive applications, Data ops (Orchestration and Tooling), ML Ops
- Data Science: Analytics, Change(Anomaly) detection, 3D Vision, Deep learning
- Technologies: KubeFlow, MLFlow, K8S, Yarn, SGE, LSF, PBS/Torque, Ignite, Hive, Impala, Presto, Vertica, ClickHouse, Cassandra, Teradata, Redshift, GreenPlum, Exadata, MSSQL, PostgreSQL, MongoDB, DynamoDB, S3, ADLS, GCS, HDFS, Spark, Flink, Hadoop, and other MapReduce existent and non existent frameworks