There is a problem about word segmentation when I use Chinese in ES. Elasticsearch is the distributed, restful search and analytics engine. You can use Elasticsearch to store, search, and manage data for Logs，Metrics，A search backend，Application monitoring，Endpoint security.
CAPTCHA stands for ‘Completely Automated Public Turing test to tell Computers and Humans Apart’. It’s already possible to solve it with the rise of deep learning and computer vision.
Flink optimization includes resource configuration optimization, back pressure processing, data skew, KafkaSource optimization and FlinkSQL optimization.
There are systems that can store values of different columns separately, but that can’t effectively process analytical queries due to their optimization for other scenarios. Examples are HBase and BigTable. You would get throughput around a hundred thousand rows per second in these systems, but not hundreds of millions of rows per second.
Data warehouse is a system that pulls together data derived from operational systems and external data sources within an organization for reporting and analysis. A data warehouse is a central repository of information that provides users with current and historical decision support information.
Data warehouse is a system that pulls together data derived from operational systems and external data sources within an organization for reporting and analysis. A data warehouse is a central repository of information that provides users with current and historical decision support information.
Data warehouse is a system that pulls together data derived from operational systems and external data sources within an organization for reporting and analysis. A data warehouse is a central repository of information that provides users with current and historical decision support information.
Apache Parquet is designed for efficient as well as performant flat columnar storage format of data compared to row based files like CSV or TSV files. Parquet uses the record shredding and assembly algorithm which is superior to simple flattening of nested namespaces. Parquet is optimized to work with complex data in bulk and features different ways for efficient data compression and encoding types. This approach is best especially for those queries that need to read certain columns from a large table. Parquet can only read the needed columns therefore greatly minimizing the IO.
