How does JusDB's archival approach differ from generic tools like pt-archiver?

Generic tools work at the row level with fixed logic — they don't understand your schema relationships, compliance requirements, or workload patterns. JusDB builds tailor-made archival pipelines for each client: we map foreign key dependencies, design batch strategies around your traffic patterns, add checksum verification at every stage, and integrate with your specific cloud storage tiers. The result is a solution engineered for your data — not a one-size-fits-all script.

Can archived data still be queried?

Yes. We export archived data in columnar formats (Parquet, ORC) optimized for analytical queries. Once in S3, GCS, or Azure Blob, the data is queryable via Amazon Athena, Google BigQuery, or Azure Synapse — giving you SQL access to historical data without loading it back into your production database.

What databases do you support for archival?

We support archival for MySQL, PostgreSQL, MongoDB, Cassandra, SQL Server, Aerospike, MariaDB, Redis, ClickHouse, and more. Each database engine has its own archival pipeline design — for example, MySQL uses row-level batch operations with InnoDB-aware locking, while Cassandra archival is coordinated around compaction cycles and tombstone management.

Is there any production downtime during archival?

No. JusDB archival pipelines use adaptive throttling — they process data in controlled batches and automatically back off when production load increases. For SQL Server, we can use partition switching for near-instant archival. All operations are monitored by our SRE team in real time.

How do you handle data with complex foreign key relationships?

Our archival engine maps the complete dependency graph of your schema before archival begins. We archive parent and child records together, maintaining referential integrity throughout. Orphaned records are flagged, and cascading deletes are handled in the correct order with transaction-level consistency.

What compliance standards does JusDB's archival support?

Our data lifecycle management supports GDPR (including right-to-be-forgotten automation), HIPAA, SOC 2, PCI-DSS, and RBI requirements. Every archival and purge operation generates a cryptographic audit trail. Retention policies are enforced automatically with legal hold overrides available.

How much can we save on storage costs?

Clients typically see 60-80% reduction in database storage costs. The exact savings depend on your data profile — we run a cost projection model during the assessment phase showing projected savings across storage tiers. Moving 1 TB from RDS to S3 Glacier can save $200+/month alone.

Can JusDB manage archival as an ongoing service?

Yes. Most clients include archival management in their JusDB managed service plan. Our SREs handle automated partition rotation, archival schedule execution, monitoring, and monthly reporting — so archival runs continuously without your team needing to manage it.

Data Lifecycle Management

Database Archival & Partition Management

In short: Database archival and partition management is the practice of moving aged, infrequently accessed data out of active tables into intelligent partitions and cost-effective cold storage (AWS S3, GCP Cloud Storage, Azure Blob) — keeping it queryable and compliant while cutting storage cost and speeding up production queries.

Tailor-made archival solutions engineered for your data. Zero downtime. 70% storage savings. Every pipeline custom-built by JusDB SREs for your schema, workload, and compliance needs.

70%

Storage Cost Reduction

10B+

Rows Archived

Downtime Minutes

60%

Query Performance Gain

100%

Compliance Coverage

<15m

Response Time

The Data Growth Problem

Every production database faces the same trajectory — unchecked growth that degrades performance, inflates costs, and creates compliance risk.

Uncontrolled Data Growth

Production tables growing 50-200 GB monthly, increasing storage costs, slowing queries, and making backups take hours instead of minutes.

Query Performance Degradation

Scans across billions of historical rows slow active queries to seconds. Indexes bloat, buffer pools waste memory on data nobody reads.

Runaway Storage Costs

Paying SSD-tier pricing for data accessed once a year. Cloud storage bills grow 30-60% annually while 70%+ of data is cold.

Compliance & Retention Risk

No automated enforcement of data retention policies. Regulatory audits expose gaps — data kept too long or purged too early.

Tailor-Made Solutions

JusDB Archival Solutions

Every pipeline is custom-engineered by our SREs for your specific data model, infrastructure, and business requirements — not a wrapper around generic tools.

JusDB Archival Engine

10B+

Rows archived

Our tailor-made archival pipelines built for each client's schema, workload, and compliance needs. Unlike generic tools, every archival job is engineered specifically for your data model — handling foreign keys, soft deletes, audit trails, and referential integrity automatically.

Custom-built archival jobs per table topology
Foreign key-aware cascading archival
Zero-downtime batch processing with adaptive throttling
Checksum verification at every stage
Automatic rollback on integrity violations
Configurable retention policies per table or partition

Intelligent Partition Management

60%

Query speedup

JusDB designs and implements partitioning strategies tailored to your access patterns — not template-based configurations. We analyze query workloads, growth rates, and maintenance windows to build partition schemes that keep production fast.

Workload-aware partition design (range, hash, list, composite)
Automated partition rotation and pruning
Online partition operations with zero application changes
Partition-level backup and recovery
Cross-engine support: MySQL, PostgreSQL, MongoDB, Cassandra, MSSQL
Monitoring and alerting for partition health

Cold Storage Migration Framework

70%

Storage savings

Purpose-built pipelines that move aged data from expensive database storage to cost-effective object stores — AWS S3, GCP Cloud Storage, or Azure Blob — while keeping it queryable on demand.

Tiered storage migration (hot → warm → cold → glacier)
Compressed columnar export (Parquet, ORC, Avro)
Queryable archives via Athena, BigQuery, or Synapse
Encryption at rest and in transit (AES-256, KMS integration)
Cost modeling: projected savings before migration begins
Automated lifecycle policies synced with cloud storage tiers

Compliance-First Data Lifecycle

100%

Audit-ready

Automated enforcement of data retention and purging policies that satisfy GDPR, HIPAA, SOC 2, PCI-DSS, and RBI requirements — without manual intervention or human error.

Policy engine with per-table retention rules
Cryptographic audit trail for every archival and purge operation
Scheduled purge jobs with legal hold overrides
Data residency controls for multi-region deployments
Compliance reporting dashboards
Right-to-be-forgotten (RTBF) automation

Our 5-Phase Archival Methodology

From assessment to ongoing management — a proven process that eliminates risk and delivers measurable results.

Data Landscape Assessment

We profile every table — growth rate, access frequency, referential dependencies, and compliance classification. You get a clear map of what's hot, warm, and cold.

Table growth analysis

Access pattern heatmap

Dependency graph

Compliance classification matrix

Archival Strategy Design

Based on the assessment, we design a tailor-made archival strategy: what to archive, when, where, and how. Every decision is documented and approved before implementation.

Archival architecture document

Partition strategy blueprint

Cost projection model

Retention policy definitions

Pipeline Engineering

Our SREs build the archival pipelines specific to your schema and infrastructure. No off-the-shelf scripts — each pipeline is tested against production-scale data in staging.

Custom archival jobs

Integrity verification suite

Staging environment validation

Performance benchmarks

Zero-Downtime Execution

Archival runs in controlled batches during your maintenance windows — or continuously with adaptive throttling that backs off when production load spikes.

Batch execution logs

Performance impact report

Checksum verification results

Storage savings report

Monitoring & Automation

Ongoing partition rotation, automated archival schedules, and alerting — so the system maintains itself. Our SREs monitor the pipelines as part of your managed service.

Automated schedules

Grafana dashboards

Alert runbooks

Monthly archival reports

Engine-Specific Archival

Each database engine has unique archival characteristics. JusDB builds engine-native solutions — not generic wrappers.

MySQL

Learn more

Range/hash/list partitioning with online DDL
Batch archival with row-level locking control
InnoDB tablespace reclamation
Binlog-safe archival operations

PostgreSQL

Learn more

Declarative and inheritance-based partitioning
pg_dump-compatible archival exports
TOAST table optimization
Vacuum-aware archival scheduling

MongoDB

Learn more

Time-series collection migration
Sharded cluster archival coordination
WiredTiger compaction after purge
TTL index management

Cassandra

Learn more

TTL and tombstone management
Compaction-aware archival timing
SSTable-level data export
Cross-datacenter archival consistency

SQL Server

Learn more

Partition switching for instant archival
Stretch Database migration
Filegroup-based tiered storage
Always Encrypted archival support

Aerospike

Learn more

Namespace-level data migration
Set-based archival with scan policies
SSD storage reclamation
Cross-cluster data movement

Cold Storage Destinations

We migrate archived data to the right storage tier on your cloud — keeping it queryable, compliant, and cost-optimized.

AWS

Query via Amazon Athena

S3 Standard
Warm archives — accessed monthly
S3 Infrequent Access
Cold archives — accessed quarterly
S3 Glacier
Compliance archives — 7+ year retention
S3 Glacier Deep Archive
Regulatory archives — rarely accessed

Google Cloud

Query via BigQuery

Cloud Storage Standard
Warm archives — frequent access
Cloud Storage Nearline
Monthly access archives
Cloud Storage Coldline
Quarterly access archives
Cloud Storage Archive
Long-term compliance archives

Microsoft Azure

Query via Azure Synapse Analytics

Blob Storage Hot
Warm archives — active queries
Blob Storage Cool
Infrequent access archives
Blob Storage Cold
Rare access, low-cost storage
Blob Storage Archive
Long-term compliance storage

CDC-Powered Pipelines

Real-Time Data Pipeline Architectures

JusDB designs and operates end-to-end CDC pipelines that move data from your OLTP databases to analytics engines, data lakes, and warehouses — in real time, with exactly-once semantics.

Real-Time Analytics with StarRocks

MySQL / PostgreSQL

OLTP Source

CDC Pipeline

Debezium / Flink CDC

Apache Kafka

Event Streaming

StarRocks

Real-Time OLAP

Use case: Sub-second dashboards, real-time reporting, ad-hoc analytics on live data

Latency: <5 second end-to-end from commit to queryable in StarRocks

Scale: 100K+ events/sec sustained throughput

Columnar Analytics with ClickHouse

MySQL / PostgreSQL

OLTP Source

CDC Pipeline

Debezium / Flink CDC

Apache Kafka

Event Streaming

ClickHouse

Columnar OLAP

Use case: Log analytics, time-series aggregation, petabyte-scale historical queries

Latency: Near real-time ingestion via Kafka Engine or MaterializedMySQL

Scale: 1M+ rows/sec ingestion, petabyte-scale storage

Data Lake on S3 with Athena

MySQL / PostgreSQL

OLTP Source

CDC Pipeline

Debezium / AWS DMS

S3 (Parquet / Iceberg)

Data Lake Storage

Amazon Athena

Serverless SQL

Use case: Cost-effective data lake, historical analytics, compliance archives queryable via SQL

Format: Parquet with Iceberg/Hudi for ACID transactions on the lake

Cost: $5/TB scanned — 90% cheaper than keeping data in RDS

Warehouse Sync to BigQuery

MySQL / PostgreSQL

OLTP Source

CDC Pipeline

Debezium / Flink CDC

Pub/Sub or Kafka

Event Streaming

BigQuery

Cloud Data Warehouse

Use case: Centralized data warehouse, BI dashboards, ML feature pipelines

Sync: Streaming inserts or micro-batch every 1-5 minutes

Scale: Petabyte-scale, auto-scaling compute, pay-per-query

ETL / ELT Pipelines with Apache SeaTunnel

Any OLTP / File / API

100+ Connectors

Apache SeaTunnel

Distributed Data Integration

StarRocks / ClickHouse / Doris

OLAP Destinations

Use case: Batch & real-time ETL without Kafka — direct source-to-sink pipelines

Advantage: No message queue needed, lower infra cost, 100+ built-in connectors

Scale: Distributed execution on Flink / Spark / standalone engine

User-Facing Analytics with Apache Pinot

MySQL / PostgreSQL

OLTP Source

CDC Pipeline

Debezium → Kafka

Apache Kafka

Event Streaming

Apache Pinot

Real-Time OLAP

Use case: User-facing analytics, real-time leaderboards, anomaly detection dashboards

Latency: Sub-50ms p99 at 100K+ QPS — built for customer-facing queries

Advantage: Pluggable indexes (sorted, star-tree, text) for ultra-fast aggregations

Unified Analytics with Apache Doris

MySQL / PostgreSQL

OLTP Source

CDC Pipeline

Flink CDC / Debezium

Apache Doris

MPP Analytics Engine

Use case: Unified batch + real-time analytics, reporting, ad-hoc queries

Advantage: MySQL-compatible protocol — zero learning curve, direct Flink CDC ingestion

Scale: Petabyte-scale, auto-compaction, materialized views for pre-aggregation

Lakehouse Architecture with Databricks

Any OLTP Source

MySQL / PostgreSQL / MongoDB

CDC Pipeline

Debezium → Kafka

Delta Lake (S3 / ADLS)

Lakehouse Storage

Databricks

Unified Analytics + ML

Use case: Unified data + AI platform — analytics, ML training, feature engineering

Format: Delta Lake with ACID transactions, time travel, and schema evolution

Scale: Exabyte-scale, auto-scaling clusters, Photon engine for 12x speed

Search Index Sync to Elasticsearch / OpenSearch

MySQL / PostgreSQL / MongoDB

OLTP Source

CDC Pipeline

Debezium + Kafka Connect

Elasticsearch / OpenSearch

Full-Text Search

Use case: Product search, autocomplete, log search synced from primary DB

Latency: <2 second from DB commit to search-indexable

Benefit: No dual-write complexity, single source of truth in OLTP

Cache Invalidation via CDC to Redis / Valkey

MySQL / PostgreSQL

OLTP Source

CDC Pipeline

Debezium

Redis / Valkey

Cache Layer

Use case: Automatic cache invalidation on DB changes — no stale data

Latency: <100ms from commit to cache update

Benefit: Eliminates TTL guesswork and cache-aside complexity

Cross-Database Sync — MongoDB to PostgreSQL

MongoDB

Document Store

CDC Pipeline

Debezium MongoDB Connector

PostgreSQL

Relational Reporting

Use case: Flatten MongoDB documents into relational tables for reporting & BI

Transform: JSON → relational schema mapping with custom transformers

Benefit: Best of both worlds — flexible writes, structured reads

Multi-Source Unified Data Lake

Enterprise

MySQL

PostgreSQL

MongoDB

Cassandra

JusDB CDC Platform

Multi-Connector Orchestration

Apache Kafka

Unified Event Bus

StarRocks

S3 + Athena

Elasticsearch

Redis Cache

Use case: Unified data platform — all databases feeding all consumers via one CDC bus

Architecture: Event-driven microservices, CQRS, event sourcing patterns

Managed by: JusDB SREs — Kafka, connectors, schema registry, monitoring

All Supported Pipeline Patterns

Source	CDC Engine	Destination	Pattern
MySQL	Debezium / Flink CDC	StarRocks	Real-time OLAP
PostgreSQL	Debezium / Flink CDC	StarRocks	Real-time OLAP
MySQL	Debezium / Flink CDC	ClickHouse	Columnar analytics
PostgreSQL	Debezium / Flink CDC	ClickHouse	Columnar analytics
MySQL	Debezium / AWS DMS	S3 → Athena	Serverless data lake
PostgreSQL	Debezium / AWS DMS	S3 → Athena	Serverless data lake
MySQL	Debezium / Flink CDC	BigQuery	Cloud warehouse
PostgreSQL	Debezium / Flink CDC	BigQuery	Cloud warehouse
Any Source	Apache SeaTunnel	StarRocks / ClickHouse / Doris	ETL / ELT
MySQL / PostgreSQL	Flink CDC / Debezium	Apache Doris	MPP analytics
MySQL / PostgreSQL	Debezium → Kafka	Apache Pinot	User-facing OLAP
Any OLTP	Debezium → Kafka	Databricks (Delta Lake)	Lakehouse + ML
MySQL / PostgreSQL	Debezium	Elasticsearch	Search index sync
MongoDB	Debezium	Elasticsearch / OpenSearch	Search index sync
MySQL / PostgreSQL	Debezium	Redis / Valkey	Cache invalidation
MongoDB	Debezium	PostgreSQL	Cross-DB materialization
Cassandra	CDC / Debezium	S3 → Athena	Archival + analytics
MySQL / PostgreSQL	AWS DMS	Redshift	Cloud warehouse
MySQL / PostgreSQL	Debezium	TiDB (TiFlash)	HTAP analytics
Any OLTP	Debezium	Apache Iceberg (S3)	Lakehouse
Any OLTP	Debezium	Delta Lake (S3)	Lakehouse

Real-World Archival Scenarios

How JusDB's archival solutions solve critical data challenges across industries.

Fintech

Transaction History Archival

A fintech platform with 2B+ transaction records growing 100M/month. Active queries scanned years of history, taking 8-12 seconds.

Storage reduced4.2 TB → 800 GB active

Query latency8s → 200ms

Monthly savings$12,000+

E-Commerce

Order & Inventory Lifecycle

An e-commerce platform with 500M+ order records across MySQL and MongoDB. Backups took 6+ hours, and storage costs doubled annually.

Backup time6h → 45min

Storage cost68% reduction

ComplianceAutomated 7-year retention

SaaS

Audit Log & Telemetry Archival

A SaaS platform generating 50M+ audit events daily across PostgreSQL and Elasticsearch. SOC 2 required 3-year retention with sub-second query access.

Events archived18B+ rows to S3 Parquet

Query accessVia Athena, <2s

SOC 2 auditPassed — zero findings

Why Tailor-Made Beats Generic

Off-the-shelf archival tools handle simple cases. Real production databases need engineered solutions.

Capability	Generic Tools	JusDB Solutions
Schema-aware archival	Manual configuration	Auto-mapped dependencies
Foreign key handling	User responsibility	Cascading archival engine
Adaptive throttling	Fixed batch sizes	Load-aware auto-scaling
Multi-engine support	Engine-specific tools	Unified pipeline framework
Cold storage migration	Not included	S3 / GCS / Azure Blob
Compliance automation	Not included	GDPR, HIPAA, SOC 2, PCI-DSS
Integrity verification	Basic checksums	Multi-stage verification + rollback
Ongoing management	DIY scripts	SRE-managed 24/7
Queryable archives	Not supported	Athena / BigQuery / Synapse

Frequently Asked Questions

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