Towards Data Operations
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The document discusses integrating data science workflows with continuous integration and delivery (CICD) practices, known as Data Operations or DataOps. It outlines challenges in traditional data science workflows around data versioning, reproducibility, and delivering value incrementally. Key aspects of CICD for data and models are described, including continuous data quality assessment, model tuning, and deployment. The Data-Mill project is introduced as an open-source platform for enforcing DataOps principles on Kubernetes clusters through modular "flavors" of software components and built-in exploration environments.
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Towards Data Operations
- 1. Towards Data Operations Dr. Andrea Monacchi Streaming data, models and code as first-class citizens
- 2. Riservato Nome personalizzato dell'azienda Versione 1.0 1. About Myself 2. Big Data Architectures 3. Stream-based computing 4. Integrating the Data Science workflow Summary
- 3. About ● BS Computer Science (2010) ● MS Computer Science (2012) ● PhD Information Technology (2016) ● Consultancy experience @ Reply DE (2016-2018) ● Independent Consultant
- 5. Big Data: why are we here? Loads: ● Transactional (OLTP) ○ all operations ○ ACID properties: atomicity, consistency, isolation, and durability ● Analytical (OLAP) ○ append-heavy loads ○ aggregations and explorative queries (analytics) ○ hierarchical indexing (OLAP hyper-cubes) Scalability: ● ACID properties costly ● CAP Theorem ○ impossible to simultaneously distributed data load and fulfill 3 properties: consistency, availability, partition tolerance (tolerance to communication errors) ○ CA are classic RDBMS - vertical scaling only ○ CP (e.g. quorum-based) and AP are NoSQL DBs ○ e.g. Dynamo DB (eventual consistency, AP) ● NoSQL databases ○ relax ACID properties to achieve horizontal scaling
- 6. Scalability ● Key/Value Storage (DHT) ○ decentralised, scalable, fault-tolerant ○ easy to partition and distribute data by key ■ e.g. p = hash(k) % num_partitions ○ replication (partition redundancy) ■ 2: error detection ■ 3: error recovery ● Parallel collections ○ e.g. Spark RDD based on underlying HDFS blocks ○ master-slave (or driver-worker) coordination ○ no shared variables (accumulators, broadcast vars) Parallel computation: 1. Split dataset into multiple partitions/shards 2. Independently process each partition 3. Combine partitions into result ● MapReduce (Functional Programming) ● Split-apply-combine ● Google’s MapReduce
- 7. ● Cluster Manager ○ Yarn, Mesos, K8s ○ resource isolation and scheduling ○ security, fault-tolerance, monitoring ● Data Serialization formats ○ Text: CSV, JSON, XML,.. ○ Binary: SeqFile, Avro, Parquet, ORC, .. ● Batch Processing ○ Hadoop MapReduce variants ■ Pig, Hive ○ Apache Spark ○ Python Dask ● Workflow management tools ○ Fault-tolerant task coordination ○ Oozie, Airflow, Argo (K8s) Architectures for Data Analytics ● Stages ○ Ingestion (with retention) ○ (re)-processing ○ Presentation/Serving (indexed data, OLAP) ● Lambda Vs. Kappa architecture ○ batch for correctness, streaming for speed ○ mix of technologies (CAP theorem) ○ complexity & operational costs
- 9. 1st phase - Ingestion: MQTT ● Pub/Sub ○ Totally uncoupled clients ○ messages can be explicitly retained (flag) ● QoS ○ multilevel (0: at most once, 1: at least once, 2: exactly once) ● Persistent sessions ○ broker keeps further info of clients to speed up reconnections ○ queuing of messages in QoS 1 and 2 (disconnections) ○ queuing of all acks for QoS 2 ● Last will and Testament messages (LWT) ○ kept until proper client disconnect is sent to the broker
- 10. ● fault-tolerant pub/sub system with message retention ● exactly once semantics (from v0.11.0) using transactional.id flag (and acks) ● topic as an append-only file log ○ ordering by arrival time (offset) ○ stores changes on the data source (deltas) ○ topic/log consists of multiple partitions ■ partitioning instructed by producer ■ guaranteed message ordering within partition ■ based on message key ● hash(k) % num_partitions ● if k == null, then round robin is used ■ distribution and replication by partition ● 1 elected active (leader) and n replicas ● Zookeeper 1st phase - Ingestion: Kafka P Broker2 Broker1 Partition0 Partition1 Partition2 Partition3 Broker3 Partition4 Partition5
- 11. ● topic maps to local directory ○ 1 file created per each topic partition ○ log rolling: upon size or time limit partition file is rolled and new one is created ■ log.roll.ms or log.roll.hours = 168 ○ log retention: ■ save rolled log to segment files ■ older segments deleted log.retention.hours or log.retention.bytes ○ log compaction: ■ deletion of older records by key (latest value) ■ log.cleanup.policy=compact on topic ● number of topic partitions (howto) ○ more data -> more partitions ○ proportional to desired throughput ○ overhead for open file handles and TCP connections ■ n partitions, each with m replicas, n*m ■ total partitions on a broker < 100 * num_brokers * replication_factor, (divide for all topics) 1st phase - Ingestion: Kafka
- 12. ● APIs ○ consumer / producer (single thread) ○ Kafka connect ○ KSQL ● Producer (Stateless wrt Broker) ○ ProducerRecord: (topic : String, partition : int, timestamp : long, key : K, value : V) ○ partition id and time are optional (for manual setup) ● Kafka Connect ○ API for connectors (Source Vs Sink) ○ automatic offset management (commit and restore) ○ at least once delivery ○ exactly once only for certain connectors ○ standalone Vs. distributed modes ○ connectors configurable via REST interface 1st phase - Ingestion: Kafka ● Consumer ○ stateful (own offset wrt topic’s) ○ earliest Vs. latest recovery ○ offset committing: manual, periodical (default 5 secs) ○ consumers can be organized in load-balanced groups (per partition) ○ ideally: consumer’s threads = topic partitions
- 13. 2nd phase - Stream processing ● streams ○ bounded - can be ingested and batch processed ○ unbounded - processed per event ● stream partitions ○ unit of parallelism ● stream and table duality ○ log changes <-> table ○ Declarative SQL APIs (e.g. KSQL, TableAPI) ● time ○ event time, ingestion time, processing time ○ late message handling (pre-buffering) ● windowing ○ bounded aggregations ○ time or data driven (e.g. count) windows ○ tumbling, sliding and session windows ● stateful operations/transformations ○ state: intermediate result, in-memory key-value store used across windows or microbatches ○ e.g. RocksDB (Flink+KafkaStreams), LSM-tree ○ e.g. changeLogging back to Kafka topic (KafkaStreams) ● checkpointing ○ save app status for failure recovery (stream replay) ● frameworks ○ Spark Streaming (u-batching), Kafka Streams and Flink ○ Apache Storm, Apache Samza
- 14. Code Examples Flink ● processing webserver logs for frauds ● count downloaded assets per user ● https://github.com/pilillo/flink-quickstart ● Deploy to Kubernetes SparkStreaming & Kafka ● https://github.com/pilillo/sparkstreaming-quickstart Kafka Streams ● project skeleton
- 16. Data Science workflow Technical gaps potentially resulting from this process! ● Data Analytics projects ○ stream of research questions and feedback ● Data forked for exploration ○ Data versioning ○ Periodic data quality assessment ● Team misalignment ○ scientists working aside dev team ○ information gaps w/ team & stakeholders ○ unexpected behaviors upon changes ● Results may not be reproducible ● Releases are not frequent ○ Value misalignment (waste of resources) ● CICD only used for data preparation
- 17. Data Operations (DataOps) ● DevOps approaches ○ lean product development (continuous feedback and value delivery) using CICD approaches ○ cross-functional teams with mix of development & operations skillset ● DataOps ○ devops for streaming data and analytics as a manufacturing process - Manifesto, Cookbook ○ mix of data engineering and data science skill set ○ focus: continuous data quality assessment, model reproducibility, incremental/progressive delivery of value
- 18. Data Operations workflow Data Science workflow
- 19. CICD activities
- 20. CICD for ML
- 21. ● Continuous Data Quality Assessment ○ data versioning (e.g. apache pachyderm) ○ syntax (e.g. confluent schema registry) ○ semantic (e.g. apache griffin) - accuracy, completeness, timeliness, uniqueness, validity, consistency ● Model Tuning ○ hyperparameters tuning - black-box optimization ○ autoML - model selection ○ continuous performance evaluation (wrt newer input data) ○ stakeholder/user performance (e.g. AB testing) ● Model Deployment ○ TF-serve, Seldon ● ML workflow management ○ Amazon Sagemaker, Google ML Engine, MLflow, Kubeflow, Polyaxon CICD for ML code See also: https://github.com/EthicalML/awesome-machine-learning-operations
- 22. Data-Mill project ● Based on Kubernetes ○ open and scalable ○ seamless integration of bare-metal and cloud-provided clusters ● Enforcing DataOps principles ○ continuous asset monitoring (code, data, models) ○ open-source tools to reproduce and serve models ● Flavour-based organization of components ○ flavour = cluster_spec + SW_components ● Built-in exploration environments (dashboarding tools, jupyter notebooks with DS libraries) https://data-mill-cloud.github.io/data-mill/
- 23. Thank you!