v0.1.6

release: bump project version to 0.1.6

Signed-off-by: Lalith Suresh <suresh.lalith@gmail.com>

v0.1.5

Fixed

• Fixes a regression in the CSV parser
  (#801)

v0.1.4

Added

• WebConsole: Add Kafka Authentication options for connectors
  (#614)
• WebConsole: Add breadcrumbs for all pages
  (#622)
• WebConsole: Add ability to edit connectors from Pipeline Builder
  (#664)

v0.1.3

Fixed

• SecOps demo: Fixes a regression in the SecOps demo related to auto-commit behavior (#667).

Added

• WebConsole: The pipeline view now also shows a graph of memory utilization over time (#610)

v0.1.2

Bug fixes

• #593: Investigate zstd usage and compilation times
• #594: Topic names gets lost in the Kafka input connector configuration dialogue
• #596: Documentation links in WebConsole home should point to feldera.com/docs
• #597: Docker logs should print localhost:8080 instead 0.0.0.0:8080
• #598: Docker logs should point documentation to feldera.com/docs
• #602: Remove the Auto Offset Reset option from the Kafka output connector config
• #612: Failure to delete ingress data rows
• #624: Create table with duplicate column name with different types results in generic editor highlighting
• #633: The compiler rejects some seemingly valid column names
• #636: Incorrect column name case sensitivity check in the compiler

v0.1.1

Bug fixes:

• #575: Reduce logging when running demo via docker-compose.
• Docker compose now exposes the pipeline-manager on port 8080.

v0.1.0

Feldera v0.1.0 release

We are happy to announce the first developer preview release of the Feldera Continuous Analytics Platform.

What is Feldera?

Feldera is a real-time analytics system based on three key principles:

• Continuous analytics: Feldera evaluates queries continuously, updating their results as input data changes.
• Incremental evaluation: A Feldera pipeline transforms a stream of input changes to SQL tables into a stream of output changes to SQL views. Internally, it performs a minimal amount of work to update query results incrementally, without complete re-computation. See our VLDB'23 paper for a rigorous description of this technique.
• Computation over data in motion: Feldera allows users to run continuous queries directly on data in motion, without storing the data in databases or storage systems before querying.

In this release

• 💻 Install and run Feldera with a single command.
• 📋 Write tables and queries using standard SQL.
• 🔀 Stream data to and from Feldera via HTTP or Kafka/Redpanda.
• 🔧 Manage and monitor continuous analytics pipelines from the Feldera Web Console or using the REST API.

Coming soon

• Time series analytics. Continuously evaluate window-based queries over time series data.

Roadmap

• Storage: Feldera currently evaluates queries in memory. We are working on adding persistent storage support, which will enable Feldera to run workloads that scale beyond main memory.
• Fault tolerance and scale-out: Run Feldera pipelines with high availability across multiple hosts.
• Connectors for popular databases: Ingest data from and send query results to Postgres, MySQL, Snowflake, and others.
• Cloud: Run Feldera in your Virtual Private Cloud or consume it as a public cloud SaaS.

Test release for 0.1.0

Test release for 0.1.0