Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 1/28: Stream ingestion and pub/sub systems Streaming sources Files, e.g. transaction logs Sockets IoT devices and

data silos or integration bottlenecks. A modern Real-Time UDL typically includes: Real-time data ingestion from structured, semi-structured and unstructured sources (IoT, logs, event streams). Multi-model real- time insights, delaying critical business actions. Optimized Ingestion, Indexing and Querying Real-Time UDLs index data upon ingestion and execute complex queries on very large data sets in the sub-second for many applications. Continuous data flow: CrateDB handles high-throughput, high-velocity data ingestion and analysis, ensuring businesses can respond dynamically to changing conditions. Instant data insights:

Multiple SCD models (slides after that...) • Good “dove-tailing” with subsequent “sematic” ETL steps Ingestion into relational data lake semantic layer (cubes, marts etc..) ODS, e.g. redshift, greenplum uncompacted data in target S3 • HVR also delivers meta-data as hive external tables definitions Ingestion into data lake storage compaction storage, e.g. CSV or Avro files on S3 semantic layer (cubes need SQL access will get ‘basic’ replication Added value from HVR • Easy to setup • Performant Ingestion into data lake using streaming compaction semantic layer (cubes, marts etc..) ETL ODS, e.g

test input.18 Aurora Innovation Higher-level tests in practice Safely rewriting a critical data ingestion pipeline 500,000 + lines of code 12,000 + real-world datasets 10,000 + attributes to verify messages:[MessageBuffer]; } root_type Messages;52 Aurora Innovation Data ingestion w/ async processing53 Aurora Innovation Data ingestion w/ on-demand processing54 Aurora Innovation Data Retention Local Filesystem

8388608 chunk_target_size: 8388608 ingestion_rate_mb: 8 ingestion_burst_size_mb: 16 2. 将 loki.yaml 中的更改应用到 Loki 服务器。 loki.yaml 文件示例 文件示例 429 Too Many Requests Ingestion rate limit exceeded (limit: 8388608 bytes/sec) while attempting to ingest '2140' lines totaling '3285284' bytes 429 Too Many Requests Ingestion rate limit exceeded' or '500 Internal Server Error rpc error: code = ResourceExhausted desc = grpc: filesystem limits_config: reject_old_samples: true reject_old_samples_max_age: 12h ingestion_rate_mb: 8 ingestion_burst_size_mb: 16 chunk_store_config: max_look_back_period: 0s table_manager:

build dynamic tables for both streaming and batch processing in Flink, sup- porting high-speed data ingestion and timely data query. Tip: This article assumes that you have mastered the basic knowledge and build dynamic tables for both streaming and batch processing in Flink, sup- porting high-speed data ingestion and timely data query. Tip: This article assumes that you have mastered the basic knowledge and build dynamic tables for both streaming and batch processing in Flink, sup- porting high-speed data ingestion and timely data query. Tip: This article assumes that you have mastered the basic knowledge and

(Incubating) Apache Paimon(incubating) is a streaming data lake platform that supports high-speed data ingestion, change data tracking and efficient real-time analytics. Tip: This article assumes that you have (Incubating) Apache Paimon (Incubating) is a streaming data lake platform that supports high-speed data ingestion, change data tracking, and efficient real-time analytics. Tip: This article assumes that you have (Incubating) Apache Paimon(incubating) is a streaming data lake platform that supports high-speed data ingestion, change data tracking and efficient real-time analytics. Tip: This article assumes that you have

(Incubating) Apache Paimon(incubating) is a streaming data lake platform that supports high-speed data ingestion, change data tracking and efficient real-time analytics. Tip: This article assumes that you have (Incubating) Apache Paimon (Incubating) is a streaming data lake platform that supports high-speed data ingestion, change data tracking, and efficient real-time analytics. Tip: This article assumes that you have (Incubating) Apache Paimon(incubating) is a streaming data lake platform that supports high-speed data ingestion, change data tracking and efficient real-time analytics. Tip: This article assumes that you have

(Incubating) Apache Paimon(incubating) is a streaming data lake platform that supports high-speed data ingestion, change data tracking and efficient real-time analytics. Tip: This article assumes that you have (Incubating) Apache Paimon (Incubating) is a streaming data lake platform that supports high-speed data ingestion, change data tracking, and efficient real-time analytics. Tip: This article assumes that you have (Incubating) Apache Paimon(incubating) is a streaming data lake platform that supports high-speed data ingestion, change data tracking and efficient real-time analytics. Tip: This article assumes that you have

build dynamic tables for both streaming and batch processing in Flink, sup- porting high-speed data ingestion and timely data query. Tip: This article assumes that you have mastered the basic knowledge and build dynamic tables for both streaming and batch processing in Flink, sup- porting high-speed data ingestion and timely data query. Tip: This article assumes that you have mastered the basic knowledge and build dynamic tables for both streaming and batch processing in Flink, sup- porting high-speed data ingestion and timely data query. Tip: This article assumes that you have mastered the basic knowledge and