Vector store maintenance + data movement

Vector store maintenance and data

movement. Big idea, one sentence.

Vectors go stale fast. So you need

incremental updates, change detection,

and scheduled refresh pipelines, plus

reliable ways to move enterprise data

into AWS. Mon why maintenance matters.

Exam framing. AWS assumes documents

change, permissions change, policies

update, duplicates appear, embeddings

drift. If your design says rebuild

everything, it's usually wrong.

Incremental updates don't re-mbed the

universe. What it means? Only changed

content is rechunked, re-mbbedded,

re-indexed. Unchanged content is left

alone. How it's commonly done. Track

document IDs, hash version, compare new

versus old, update only deltas. Where

this applies, open search vector

indexes, Aurora PG vector tables, manage

KB ingestion jobs. Exam signal. If you

see avoid unnecessary recomputation,

reduce cost, large corpus, incremental

updates. Three, change detection. Know

what changed? Change detection answers.

Do I need to re-mbed this document?

Have common mechanisms. File checksum

hash last modified timestamp version ID

S3 CDC for databases pattern

exam trap. Re-mbbed all documents

nightly. Detect changes and update

selectively. Four, scheduled refresh

pipelines. Time-based hygiene. Not

everything is event driven. Some

pipelines run on a schedule. Nightly,

weekly, monthly. Use cases. Compliance

reindexing, permission refresh, stale

embedding, cleanup, re-mbedding with a

new model. AWS services often used

eventbridgeuler, step functions, glue

jobs, exam signal, periodic refresh,

scheduled reprocessing, scheduled

pipeline five, AWS static one

maintenance edition, static update

rules, detection logic, refresh cadence,

target vector store plus one, change

data, documents, records, the rules stay

fixed, the data evolves. That's static

plus one again. Six, data movement into

AWS enterprise reality. Most enterprise

data does not start in S3. AWS expects

you to know how it gets there. Seven,

data sync, bulk, automated transfers.

What it's for, large-scale data

transfer, file systems, S3, EFS, FSX,

on-prem or other clouds.

Characteristics, incremental sync,

preserves metadata, high throughput,

scheduled or on demand. Exam signal,

large file shares, ongoing sync,

enterprise migration, data sync. Eight,

transfer family, protocol-based

ingestion. What it's for? FTP, SFTP,

FTPS access. Partners upload files.

Legacy systems characteristics. Managed

endpoints. O via IM or directory. Files

land in S3 or EFS. Exam signal. Partners

upload via SFTP transfer family. Not a

nine. How this connects to vector stores

real pipeline. Example flow. You can see

the code in our conversation history or

you can see the code in our conversation

history.

Number 10. Classic exam traps. Watch

these. Rebuild entire vector DB nightly.

Use transfer family for bulk migration.

Ignore change detection. Vectors never

need refresh. AWS wants controlled

movement, not brute force.

One memory story. Lock it in the office

archive. Incremental updates. Only

refile documents that changed. Change

detection. Sticky note says updated.

Scheduled refresh. Monthly archive

review. Data sync. Moving filing

cabinets into the building. Transfer

family. Mail room where partners drop

files. No one reprints the entire

archive every night.

Exam compression rules. Memorize. Change

data. Rembed. Unchanged data. Skip.

Periodic hygiene. Scheduled refresh.

Bulk migration. Data sync. Data sync.

Protocol uploads. If the answer

reprocesses everything, it's probably

wrong. What AWS is really testing. They

want to know if you understand that

retrieval systems are living systems.

Designs that don't plan for change don't

survive production.

Below are clear production grade

examples you could explain in an exam

answer or whiteboard. Real examples. Day

10, vector store maintenance and data

movement. Example one, incremental

updates for a policy knowledge base.

Most common scenario. A company has

50,000 policy documents stored in S3

indexed in a vector store, Open Search

or Aurora PG Vector. Every day a few

policies are updated. Most stay

unchanged. Bad design. Re-chunk. Re-mbed

reindex all 50,000 documents nightly.

Expensive. Slow. Unnecessary. Correct.

AWS design. Incremental updates. How it

works. One, each document has document

ID, content hash, or S3 version ID. Two,

new ingestion run compares stored hash

versus current hash. Three, only

documents with changes are rechunked,

re-mbedded, updated in the vector index.

Result: 200 docs changed, only 200

updated. Cost and latency dropped

massively. Exam signal large corpus,

avoid recomputation, reduce cost,

incremental updates. Example two, change

detection using S3 versioning. Scenario,

documents are uploaded to S3 and

ingested into RAG. AWS native trick.

Enable S3 versioning. Store the version

ID with each vector entry. Change

detection logic. New upload arrives.

Compare new version ID with stored one.

If same, skip embedding. If different,

reprocess. Why AWS likes this? No custom

hashing needed. Native AWS feature. Very

exam friendly. Exam takeaway.

S3 versioning equals built-in change

detector.

Example three, scheduled refresh after

embedding model upgrade. Scenario, you

switch from Titan embeddings V1, V2, or

any new embedding model. All existing

vectors are now technically valid,

semantically outdated.

Correct AWS design, scheduled refresh.

What you do? Create a scheduled pipeline

weekly or monthly. Re-mbed documents

gradually. Replace vectors in batches.

AWS services event bridge scheduler.

Step functions. Vector store update

jobs.

Why this matters? No downtime. No sudden

cost spike. Controlled migration. Exam

signal. New embedding model. Periodic

reprocessing. Scheduled refresh

pipeline. Example four. Permission

changes without re-mbedding. Scenario. A

user loses access to a department's

documents. Wrong instinct. Re-mbed

documents. Correct design. Keep vectors

unchanged. Update metadata only. Often

in Dynamo DB or open search filters.

Why? Embeddings don't change. Access

rules do. Exam takeaway.

Not every change requires re-mbedding.

This is a big exam nuance. Example five.

Bulk ingestion from on-prem file shares.

Data sync. Scenario. A company has 20

tab of documents stored on an on-prem

NAS needs them in S3 for rag. Correct

AWS tool. Data sync Y high throughput

incremental sync preserves timestamps

and metadata designed for file systems

pipeline. You can see the code in our

conversation history. Tech exam signal

large file shares enterprise migration

data sync. Example six partner uploads

via SFTP transfer family scenario.

External partners upload documents daily

using SFTP legacy systems. Hack correct

AWS tool transfer family. How it works.

AWS hosted SFTP endpoint files land

directly in S3 event bridge triggers

ingestion pipeline. You can see the code

in our conversation history.

Exam signal partners SFTP FTP transfer

family. Example seven hybrid data sync

plus scheduled refresh very realistic

scenario. On-prem docs sync hourly

compliance requires weekly reindex

validation. Design data sync runs hourly

incremental scheduled pipeline runs

weekly. Audits vectors cleans stale

entries refreshes metadata. AWS loves

this because event driven plus scheduled

efficient plus compliant. Statics one

lock this in static update rules change

detection logic refresh schedule target

vector store plus one change documents

rules don't move data does. That's

statics one. Ultrashort exam cheat

sheet. Change doc re-mbbed. Same doc

skip. Model upgrade scheduled refresh.

Bulk file migration. Data sync. SFTP

uploads. Transfer family. Permission

change. Metadata update only.

Final memory story. The company archive

room. Incremental updates. Only replace

change folders. Change detection. Red

sticker equals reprocess. Scheduled

refresh. Monthly archive cleanup. Data

sync. moving filing cabinets into the

building. Transfer family mail room for

external deliveries. No sane company

reprints the entire archive daily.