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Below is a recommended high-level architecture for the data broker portion, given your requirements:

  1. Use Kafka for real-time data ingestion
  2. Retain ~10 days of recent data in a fast, low-latency store (the “hot” store)
  3. Move data beyond 10 days into a more permanent, cost-effective, and scalable storage (the “cold” store) for longer-term analysis

The overall goal is to keep recent data quickly queryable (for real-time or near real-time use cases) while also accumulating historical data in a cheaper, more scalable environment for trend analysis, ML training, or batch analytics.

1. Architectural Overview

A typical flow could look like this:

          ┌────────────┐     (1) Real-time ingestion
          │ Data Source│ ────────────────────────────┐
          └────────────┘                              ▼
                                              ┌─────────────────┐
                                              │   Kafka Topics   │
                                              │(raw or lightly   │
                                              │ enriched events) │
                                              └────────┬─────────┘
                                                       │
           (2) Stream Processing                 (2) Stream Processing
                      ▼                                   ▼
   ┌───────────────────────────┐            ┌───────────────────────────┐
   │    Hot Store (10 Days)    │            │ Cold Store / Data Lake    │
   │  (Low latency DB/Index)   │            │ (HDFS, S3, or Lakehouse)  │
   └──────────────┬────────────┘            └──────────────┬────────────┘
                  │                                        │
         (3) Recent, Real-Time Queries            (4) Historical & Trend Queries
                  ▼                                        ▼
           ┌─────────────┐                        ┌────────────────────┐
           │ RAG / ML /   │                        │ Batch Analytics /  │
           │  Microservices│                        │  BI Tools / ML     │
           └─────────────┘                        └────────────────────┘

Flow Explanation

  1. Data Sources publish events to Kafka.
  2. A Stream Processing layer (e.g., Kafka Streams, Apache Flink, or Spark Structured Streaming) reads from Kafka, optionally transforms/enriches data, and writes to two destinations:
    • Hot Store: A low-latency database or search engine with ~10 days retention.
    • Cold Store (Data Lake / Warehouse): For longer-term storage and analysis.
  3. Recent Data Queries hit the hot store for near real-time analytics, powering RAG pipelines or immediate dashboards.
  4. Historical & Trend Queries go to the cold store (HDFS, S3, or a Lakehouse solution) for large-scale, long-term analytics.

2. Kafka for Real-Time Ingestion

  1. Producers push events to Kafka Topics:
    • Could be from scrapers (arXiv, GitHub, news), user events, or any other real-time data feed.
  2. Topic Management:
    • Separate topics by source or data type (e.g., arxiv_raw, github_events, dns_data).
    • Retention settings in Kafka can be short (e.g., a few days) if you’re only using Kafka as a messaging backbone (the permanent storage will be elsewhere).
  3. Scalability:
    • Kafka clusters handle high throughput, support partitioning for parallel reads/writes.

3. Hot Store (Low-Latency Database or Index)

You want to store ~10 days of recent data in a system that supports:

  • Fast writes (incoming data from Kafka)
  • Low-latency queries for real-time or near real-time analytics
  • Automatic TTL (Time-To-Live) or rolling window so data older than 10 days gets removed or archived

Potential Technologies

  1. Elasticsearch / OpenSearch

    • Excellent for text-based searching, filtering, and aggregations.
    • Natural fit if you frequently do full-text queries or need near real-time search.
    • Supports rolling indices and retention policies.

  2. NoSQL Stores (e.g., Apache Cassandra)

    • Great for time-series or high-write workloads.
    • Can set TTL on rows so data naturally expires after 10 days.
    • Good for slice queries by timestamp.

  3. Time-Series DB (e.g., InfluxDB, TimescaleDB)

    • Purpose-built for time-series data with retention policies.
    • If your use case revolves heavily around time-series queries, this is ideal.

  4. OLAP Columnar Store (like ClickHouse)

    • Very fast analytical queries on recent data.
    • Built-in support for TTL and partitioning by time.

Choice depends on: query patterns, data formats, and your team’s expertise. If you expect a lot of text searching and flexible queries, Elasticsearch/OpenSearch is a strong option. For more structured time-series or numeric queries, Timescale/ClickHouse might be better.

4. Cold Store (Long-Term Storage)

After data ages out of the hot store (~10 days), or in parallel, you want it in a permanent, cost-effective storage for historical/trend analysis.

Potential Technologies

  1. Hadoop (HDFS)

    • Traditional approach for storing large volumes of data in a distributed filesystem.
    • Often used with Spark or Hive for batch analytics.

  2. Object Storage (AWS S3, GCS, Azure Blob)

    • Scalable, cheap, durable.
    • Can be queried with “serverless” solutions like Athena (AWS) or BigQuery (GCP).
    • Many modern “data lakehouse” architectures build on S3/Blob.

  3. Lakehouse Platforms (Databricks, Apache Iceberg, Delta Lake)

    • Combine low-cost data lake storage with data warehouse-like features (ACID transactions, schema evolution).
    • Simplify streaming + batch data handling with the same underlying files.

Loading Data to Cold Store

  • Stream Processing writes data directly to HDFS/S3 in partitioned form (e.g., by date/hour).
  • Alternatively, run batch jobs (e.g., daily or hourly) that read from Kafka or from the hot store to archive data to the lake.

5. Stream Processing Layer

You’ll need a processing framework that reads from Kafka, optionally enriches/filters the data, and writes to both the hot store and cold store.

  • Kafka Streams: If you prefer to stay within the Kafka ecosystem, easy to deploy as microservices.
  • Apache Flink: Great for continuous streaming with exactly-once guarantees, advanced windowing, and high throughput.
  • Spark Structured Streaming: Integrates well if you already use Spark for batch analytics or ML.

Typical Tasks

  1. Data Cleansing: Removing HTML tags, normalizing text, etc.
  2. Enrichment: Adding metadata (timestamps, geolocation, lookups).
  3. Aggregation or Pre-Indexing: Summaries or rolling metrics (e.g., daily commit counts, trending topics).
  4. Branching:
    • Write the enriched data to the hot store for immediate queries.
    • Write the same or slightly summarized data to the cold store for historical analysis.

6. Query & Analytics

6.1 Real-Time Queries (Hot Store)

  • Latency: Sub-second to a few seconds.
  • Use Cases:
    • RAG LLM queries that require the latest 10 days of content.
    • Dashboards for operations or near real-time monitoring.
    • Quick lookups (like “show me the last 1,000 GitHub commits referencing arXiv ID X”).

6.2 Historical & Trend Analysis (Cold Store)

  • Latency: Seconds to minutes for big queries, depending on compute engine.
  • Use Cases:
    • Monthly or quarterly trend reports.
    • Large-scale ML model training that needs the entire historical data set.
    • Deep analytics (like multi-year comparisons of research topics on arXiv).

7. Putting It All Together

Step-by-Step Summary:

  1. Data Ingestion
    • Multiple data sources (arXiv, GitHub, news, DNS, etc.) publish events to Kafka.
  2. Stream Processing
    • A job (Flink, Spark, or Kafka Streams) reads Kafka data, transforms or enriches it.
  3. Writing to Hot Store
    • Processed data is stored in a low-latency database (e.g., Elasticsearch, Cassandra, Timescale) with a ~10-day retention window.
    • This store is used for immediate queries, RAG pipelines, and real-time dashboards.
  4. Archiving to Cold Store
    • The same stream processing job (or a separate job) writes data to a long-term data lake or warehouse (e.g., HDFS, S3, Lakehouse).
    • This data is partitioned (e.g., by date/hour) and can accumulate indefinitely for historical analysis.
  5. Query & Analysis
    • Real-time queries hit the hot store (small, recent data).
    • Batch or historical trend queries run on the cold store with Spark, Hive, or Presto/Trino.
    • If you adopt a lakehouse approach, you can unify streaming + batch seamlessly.

8. Additional Considerations

  1. Data Retention Policies
    • Ensure your hot store automatically deletes data older than 10 days.
    • In Kafka, you can set a shorter retention if you only use Kafka as a buffer.
  2. Schema Management
    • Tools like Confluent Schema Registry (Avro/Protobuf) or built-in Spark/Flink schema evolution can help maintain consistent data structures over time.
  3. Scaling & Resource Management
    • Kafka, the hot store, and the cold store all need to scale horizontally if data volume grows quickly.
    • Container orchestration (Kubernetes) is common for dynamic scaling.
  4. Data Governance & Security
    • Role-based access to the hot store vs. cold store.
    • Encryption at rest and in transit, depending on regulatory requirements.
    • Logging & auditing for data access.
  5. Observability
    • Metrics & logs from Kafka, the stream processor, the hot store, and the cold store.
    • Tools like Prometheus + Grafana or the ELK stack for centralized monitoring.

Final Thoughts

This hybrid Lambda-like architecture (real-time + batch) or even a Kappa-like approach (if you keep everything in streaming) sets you up to handle two critical time horizons:

  • Immediate (“Hot”) Data for up to 10 days, supporting real-time retrieval and RAG LLM usage.
  • Long-Term (“Cold”) Data for historical trending and deeper analytics.

By leveraging Kafka as the backbone, a stream processing layer for dual writes, a low-latency store for recent data, and a scalable data lake/warehouse for historical data, you achieve a balanced solution that meets near-term performance needs without sacrificing the ability to handle large-scale, long-term queries.