Page History

...

To further the initial vision from https://www.oreilly.com/ideas/ubers-case-for-incremental-processing-on-hadoop through  - to define "incremental processing" - through use cases, patterns of functionality, designs, applications (like feature extraction, model training), algorithms, code, etc. and by building on other technologies and state of the art research.

...

• `experimental` is defined as 
  1. exploratory data analysis
  2. development in notebooks
  3. essentially ad-hoc choice of tools
  4. generally batch only, "one off", manual execution
  5. small data, manual sampling
  6. models are trained offline
  7. the end result being reports, diagrams, etc, 
• `production` = pretty much the opposite
  1. end result are enterprise data science applications 
  2. ran in production 
  3. with large, multi-dimensional data set`s that do not fit in RAM, logically infinite
  4. hence the algorithms / analysis must be incremental
  5. use of managed `data set`s : `data lake`s, `feature store`s
  6. models are trained onlineincrementally
     1. offline periodically and refreshed / deployed every few hours/days
  7. with awareness of `concept drift`, `distribution drift`, `adversarial attacks` and able to adapt
  8. use complex orchestration between core analysis and  decision layer, model monitoring and other application logic and business processes, some involving human interactions

...

Putting this data in kafka, separate databases, etc breaks not just the logical cohesion of the analysis but also leaves no room for global optimization.
See the premise of Weld : https://www.weld.rs/assets/weld-strata.pdf

• Lastly, there are "too many frameworks"and the programming model is too varied
  • No! Not Another Deep Learning Framework
  • https://www.mosharaf.com/wp-content/uploads/deepstack-hotos17.pdf
• Image Added

Hypothesis : We need "continuous" analytics in both the time dimension and , in the physical dimension.data dimension and in terms of programming model

• relational algebra
• linear algebra
• differential programming
• probabilistic programming
• computational graphs
• differential dataflow
• functional programming
  • lazy evaluation
  • monadic comprehensions
  • etc
• etc

Why "deep"

• In terms of algos we have deep learning, ofc.
• But also in terms of "data fabric" we need to handle multi-dimensional, heterogeneous, business rich meaning data and abstractions over data.
  
  • relational data - need #RelationalAlgebra
  • arrays / matrices / tensors - need #LinearAlgebra
  • graph data - need graph representations and algos (https://www.slideshare.net/oracle4engineer/using-graphs-for-data-analysis)

...

Activity

This page about Uber #Michelangelo https://eng.uber.com/michelangelo/ suggests there is still a distinction in the architecture and implementation (and programming model) between "batch" and "streaming" and the data is placed in distinct kinds of physical repositories between batch and continuous analyses.

...

Some of these may lead to `Hudi` HIPs, some to extensions and others to more broad solutions, beyond `Hudi` itself but where `Hudi` plays a part.

Use cases

(UC) Ability to support deletes

• Application to GDPR "right to be forgotten" requirement.
• Context : https://databricks.com/blog/2019/03/19/efficient-upserts-into-data-lakes-databricks-delta.html
• Status : wip https://github.com/apache/incubator-hudi/pull/635

(UC) Integrate Hudi with Apache Beam so that the sliding data window abstractions of beam can run on top of Parquet files incrementally updated through `Hudi`

...

Hypothesis The `sliding data window` abstraction from Apache Beam (also present in Spark and Flink) can eliminate most (all ) of the ad-hoc attempts to handle incremental data inside analysis code.

(UC) Use Hudi to build file based data lakes that are incrementally self updating

...

(data fabric)

`Hudi` works with one `data set` at a time but when building a `data lake` we need to relate `data set`s structurally and logically (business semantics) so that `feature store`s can be built from raw data

...

1. Apache Arrow
2. Dremio
3. Project DAWM WeldDAWN Weld (https://www.weld.rs/)

Resources / reading list

1. "Evaluating End-to-End Optimization for Data Analytics Applications in Weld"
   
   1. http://www.vldb.org/pvldb/vol11/p1002-palkar.pdf
   2. Image Added
2. "Bridging the Gap: Towards Optimization Across Linear and Relational Algebra"
   1. https://h2020-streamline-project.eu/wp-content/uploads/2017/11/lara.pdf
   2. Image Added
3. "Accessible Machine Learning through Data Workflow Management"
   1. https://eng.uber.com/machine-learning-data-workflow-management/
   2. Image Added