Data Pipeline & ETL Modernization Services

Cutover date in the contract
Fixed fee
Signed output parity
Legacy ETL switched off
AWS Premier Tier

As an agent-native AWS modernization firm, our ETL modernization services move the jobs your business depends on every night, out of Informatica, SSIS, DataStage, or Hadoop and onto AWS, and prove that every table, file, and feed comes out identical before a single consumer is repointed. The fee and the cutover date are both contract terms, signed before work begins.

Miss that date because of us, and the extra hours never reach your invoice. What stays behind is code your team owns, monitoring that pages the right person, and a legacy ETL server with its power finally cut.

Talk to us

Which ETL tool are you leaving, and what's forcing the timing?

That answer alone tells us where your critical path probably runs.

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AWS Partner Tier: Premier Tier
AWS Specialization: Agentic AI
Competencies incl. Data & Analytics: 7 Competencies
Validations incl. AWS Glue & EMR: 17 Validations

60–70%

Pipeline program labor hours done by agents, compared with a staffed bid for the same estate

94%

On-time record: engagements finished by the committed date or sooner

5 of 17

AWS Service Validations that sit on the pipeline path: Glue, EMR, Kinesis, MSK, and DMS

90 days

Window in which any production defect in our work is fixed at no charge

2008

Mactores' first AWS customer go-live

Top Mactores Clients

All 21 case studies →

Customers include Safaricom, Synaptics, Flipboard, Poshmark, Seagate, HP, Adani, DocuSign, KlearTrust, Tilia, Sterne Kessler and Total Expert.

Nobody schedules an ETL migration because they want one. A vendor gets acquired. A support date passes. A Hadoop cluster loses the last person who knew how to restart it. Then someone in finance notices the nightly batch finished at 7:40 a.m. again, twenty minutes after the CFO's dashboard was supposed to refresh.

The pressure now comes from a second direction as well. Gartner expects adoption of data streaming for agentic AI to pass 60% by 2028, up from under 15% in 2025. Agents can't act on numbers that arrive once a night through jobs nobody can explain. Legacy ETL modernization stopped being an infrastructure tidy-up the day AI programs started queuing behind fresher, governed data.

Why do legacy ETL migrations stall halfway?

The converter moves the code, not the meaning.

Accelerators translate mappings into Spark or SQL at impressive rates. Then the first downstream report shows a different total, because a lookup cached at 2 a.m. behaved differently from one that now runs live, and nobody ever wrote that rule down.

The schedule is the system.

Thousands of jobs depend on each other through file drops, control tables, and cron entries on a server under someone's desk. Move one job without its upstream and downstream neighbors, and the chain breaks somewhere nobody is watching.

Nobody owns the off switch.

Decommissioning sits in no one's statement of work. Twelve months on, the legacy servers still hum, someone still patches them, and the business case that promised savings gets quietly rewritten.

All three show up in month six. All three are decided in week one.

What started the clock?

An expired support contract, a vendor acquisition, a cluster nobody can patch, or agents that need data by 9 a.m.?

Name it, and we'll trace which job chains it puts at risk first.

Book a scoping call →

Can an ETL Migration Really Be Delivered on a Fixed Date?

Yes, if "finished" is defined first. A fixed date is only as honest as the acceptance rules underneath it, so Mactores writes those rules with your data owners during discovery, dataset by dataset, before any conversion. The date and the fee are then signed together, and our own overruns are never billed to you.

Ask how typical ETL migration services get priced and the answer is usually some version of jobs multiplied by hours. The trouble arrives later. Each conversion exception, each extra week of parallel running, and each job discovered in month seven adds billable time, so the proposal that looked cheapest tends to stop being cheap around the halfway mark.

Mactores can sign a fixed number because most of the labor in a pipeline program isn't people. Agents take the volume: thousands of mapping exports and packages parsed, lineage drawn for every report column, first-draft Spark and SQL produced, and output comparisons rerun after every cycle. We measure that volume at 60 to 70 percent of the hours a staffed proposal would bill. And when the program ends, the agents leave with us. What stays is AWS services, your repository, and runbooks your team already knows.

Our FDEs are the opposite of a bench. Each one is a forward-deployed specialist who has shipped production agent systems on AWS, and they work as members of your data team for the life of the program. Four calls are theirs, never an agent's: what to retire, what order to move things in, which feeds earn a stream, and whether a cutover goes ahead.

Agents · 60–70% of hours

FDEs & your team

Handled by agents (60–70% of hours): Repository parsing, column lineage, first-draft code, test datasets, output diffs

Decided by FDEs: What to retire, wave sequence, stream or batch, every go or no-go

Owned by your team: Logins to source systems, an owner who answers for each output, approvals at each stage gate

Common proposal language

The Mactores version

"Up to 80% automated conversion"

A cutover date in the contract, and our overruns on our books

A testing phase measured in weeks, not results

Per-dataset tolerances that end testing once they're met

Decommissioning "in a later phase"

Switch-off of the legacy scheduler and servers on a contracted day

Batch performance "expected to improve"

A finish time for each critical chain, measured before and after

Undiscovered jobs priced as change requests later

Every job listed up front, each with a signed keep, rebuild, or retire call

"The commitment has a consequence attached, and the consequence is ours."

Mactores, Our Commitment

Who pays for a slip depends on where it started. If it started with us, we absorb it, and no invoice follows. If it started on your side, say a firewall rule that takes a month or a sign-off meeting that keeps moving, the extra hours are billed at a rate printed in the contract before day one.

Read the terms before the pitch

The delivery clause, the overage rule, and the 90-day warranty, in plain language.

Read them first; the rest of this page will make more sense.

Read our commitment →

Which Jobs, Tools, and Deliverables Do Data Pipeline Modernization Services Cover?

Six workstreams, each ending in something you can inspect: an estate register with lineage, converted code with passing comparisons, rebuilt orchestration, streams or CDC where freshness matters, signed parity reports, and a switch-off certificate. That's the whole service, from the first repository export to the last legacy server going dark.

Is this a tool swap or a pipeline rebuild?

Neither, exactly. A tool swap moves the same nightly jobs onto a new engine and keeps every bad habit: full reloads, hard-coded paths, one 4 a.m. dependency that takes down twelve reports when it fails. A ground-up rebuild throws away business logic that took fifteen years to get right. Most ETL consulting services sell one or the other. We do the part in between: keep the rules, change the plumbing, and prove the outputs match.

Inside Mactores, this service belongs to the data platform modernization pillar. In most data platform programs, the pipelines are the long pole: a new warehouse can be ready in weeks while the jobs that feed it take quarters to untangle.

01
Job, schedule, and lineage discovery

Agents parse every mapping, workflow, session, package, script, and scheduler entry in the estate and build column-level lineage from each report field back to its source. Then twelve months of scheduler logs and query history sort the estate into what matters, what fails silently, and what has become digital furniture: still running every night, read by no one.

Deliverable

The estate register, listing every job with its lineage, its owner, and a signed keep, rebuild, or retire call.
02
Logic conversion with readable output

Spark on AWS Glue, SQL inside Amazon Redshift, or Spark on Amazon EMR: each job's shape picks its AWS ETL target. Agents produce the first draft of the code and mark whatever they can't translate faithfully, such as custom Java transformations, cached lookups, and functions that exist only in the source tool. FDEs rewrite those by hand and set the coding conventions your team will live with.

Deliverable

A pull request per wave, with every job inside it carrying the comparison test that proves it.
03
Orchestration and scheduling rebuilt

Data pipeline orchestration, meaning workflow dependencies, file-arrival triggers, and restart points, moves to Amazon MWAA, AWS Step Functions, or AWS Glue workflows. Hidden dependencies, like a job that silently waits on a control-table flag, get modeled explicitly so failures surface where someone is looking.

Deliverable

A dependency graph that matches production, plus a runbook for every critical chain.
04
Streaming and change data capture where freshness pays

Not every job needs to be real time. The ones feeding fraud checks, inventory positions, or AI agents usually do. Those move to change data capture through AWS DMS, or to a streaming data pipeline on Amazon Kinesis or Amazon MSK, with a latency target written down per feed.

Deliverable

A freshness target per dataset, measured and signed.
05
Output-parity testing and cutover

The harness takes over here. On every cycle of the parallel period, both estates receive the same inputs and each resulting table, extract, and partner file is diffed. Waves then switch in lineage order, and each one is rehearsed before its turn.

Deliverable

A go-live pack per wave: parity results, sign-offs, and the fallback plan nobody needed.
06
Observability, handover, and legacy retirement

Freshness, row volume, schema drift, and cost per job are tracked from the first AWS run, and each alert routes to a named person. After the final wave settles, the legacy servers, licenses, and scheduler are shut down for good.

Deliverable

Written confirmation that the legacy servers, licenses, and scheduler are gone, plus a monthly cost figure for each new pipeline.

Pipelines and warehouses tend to retire together. If your jobs load a Teradata, Netezza, or Oracle warehouse that's also headed for Amazon Redshift, our data warehouse migration services team plans both moves as one program, so the first load into Redshift comes from a pipeline that has already been proven.

How does each legacy ETL tool change the migration plan?

PowerCenter, SSIS, DataStage, and Hadoop estates don't break in the same places. An Informatica to AWS Glue migration is mostly about reusable logic and lookup behavior, while an SSIS exit is mostly about C# and file shares. The table below is where the planning conversation starts.

Legacy tool

Where the effort hides

Typical AWS destination

Informatica PowerCenter

Reusable mapplets, parameter files, session overrides, and lookup caching behavior nobody documented

AWS Glue for Apache Spark or Redshift SQL, orchestrated in Amazon MWAA; mapplets become shared, tested modules

Microsoft SSIS

C# script tasks, package configurations, SQL Server Agent schedules, and data flows tied to on-premises file shares

AWS Glue jobs or Redshift stored procedures, with schedules in AWS Step Functions or Amazon MWAA

IBM DataStage

Parallel job partitioning, transformer stages, and sequence jobs with intricate restart logic

Spark on AWS Glue or Amazon EMR, with partitioning redesigned for the target rather than copied across

Talend

Generated Java with hand edits, context variables, and components orphaned when the free edition was retired

AWS Glue or Spark on Amazon EMR, with generated code replaced by maintainable modules

Ab Initio, SAS DI, and Oracle Data Integrator

Graph and macro languages few converters understand, with logic spread across the tool and the database

Rules pulled out and reviewed with job owners, then rebuilt as Spark or SQL; anything with no reader simply stops

Hadoop, Hive, and Oozie

Hive UDFs, Oozie coordinators, small-file problems, and thousands of partitions nobody remembers creating

Amazon EMR or AWS Glue over Amazon S3 with Apache Iceberg tables, orchestrated in Amazon MWAA

AWS Data Pipeline and cron scripts

A service closed to new customers, plus shell and Python scripts scheduled on a single server

AWS Glue, AWS Step Functions, or Amazon MWAA, in line with AWS's own migration guidance

Expect the risk to be lopsided. Most PowerCenter mappings are simple and move almost mechanically; a small core of shared mapplets and lookups is where the surprises live. SSIS keeps its surprises in C# script tasks, and Hadoop keeps them in Oozie coordinators and Hive UDFs, which is why a Hadoop to EMR migration is usually planned around them. We price and date the program from that core, not from the job count.

Several of these platforms are also running out of road. PowerCenter 10.5 has passed its end-of-support date, and Informatica itself now sits inside Salesforce. SQL Server 2016, home to a large share of the SSIS packages still in production, has reached the end of Microsoft support. The free edition of Talend is gone, and AWS Data Pipeline no longer accepts new customers. None of this forces a panicked move. Each one does turn "someday" into a line on next year's risk register.

Send the scheduler export

A job list with a year of run history beats any questionnaire.

We'll reply with your critical chains and the jobs we'd expect to give trouble.

Map my ETL estate →

Do All Legacy ETL Jobs Need to Be Rewritten for AWS?

No. A full rewrite is the exception. Most jobs fall into one of five buckets: convert to AWS Glue or Redshift SQL, lift Spark onto Amazon EMR, re-architect into a stream or zero-ETL feed, merge with a near-duplicate, or switch off. Each job's bucket is decided from evidence and signed by its owner.

The decision for each job goes into a log with three fields: the evidence (run history, consumers, code complexity), the call, and the owner who signed it. That log is our data pipeline modernization strategy, and it's what keeps an ETL migration to cloud platforms from sliding toward either extreme: a literal copy of fifteen years of full reloads, or a redesign that delays everything.

Option

What it means

Good fit

Main risk

Lift

Spark, Hive, or Hadoop jobs moved onto Amazon EMR with few code changes

Good fit: The Spark is healthy and the support deadline won't wait

Main risk: Old habits, like fixed cluster sizes and full reloads, survive the move

Convert

Job logic rewritten for AWS Glue for Spark or Redshift SQL, with its business rules preserved

Good fit: Stable jobs with active consumers

Main risk: Code that passes tests but reads like a machine wrote it; FDE review on each wave keeps it maintainable

Re-architect

Batch replaced by change data capture, streaming, or zero-ETL integration, and ETL moved to ELT inside the warehouse

Good fit: Feeds where freshness drives revenue, risk, or agent decisions

Main risk: Streaming has a real run cost, so each feed gets a latency target before it gets a stream

Consolidate

Near-duplicate jobs merged into shared, parameterized modules

Good fit: Estates where ten teams built ten versions of the same customer extract

Main risk: Every owner of a copy has to agree on one definition

Retire

Switched off, with its history archived

Good fit: Twelve months of logs show no reader

Main risk: Records you're legally required to keep, so archiving follows your retention policy

Where the target warehouse itself is being redesigned so AI agents can query it, the data warehouse modernization team defines the models these pipelines load, and both programs run from a single wave plan.

Where do zero-ETL and ELT fit?

Both are real options now, and both get oversold. AWS zero-ETL integrations replicate operational databases such as Aurora and DynamoDB straight into Redshift, with no job to maintain in between, and that retires a whole class of extract work. An ETL to ELT modernization, where raw data lands first and transformation runs inside the warehouse or lakehouse, often simplifies what's left. Neither removes the need to know what the old job actually did. A zero-ETL feed that skips a cleansing rule the business relied on is just a faster way to publish a wrong number.

Got an automation percentage?

A high auto-conversion figure says nothing about which outputs will match.

Send the proposal, and we'll mark the jobs it's likely to get wrong.

Review my conversion plan →

What Are the Stages of an ETL Migration, and Who Signs Off on Each?

Five stages, each closed by your data owners: Discover, which ends in a signed estate register and a contracted date; Pilot, which moves the hardest chain first; Migrate, in lineage-ordered waves; Reconcile, where every output is compared; and Retire, which switches the legacy estate off. Reports keep working throughout.

One special case is extract files produced by JCL batch on z/OS. When those producers are also leaving the mainframe, our mainframe modernization services team schedules their waves against yours, so no pipeline is ever waiting on a file whose format changed underneath it.

01

Discover

Discovery is mostly reading, and agents do most of it. The output that matters is a short list: the jobs that carry real risk, each reviewed with its owner, and the architecture and wave order that follow from them. The stage closes with your ETL migration methodology on paper, with a price and a date attached.

Stage exit

Register, parity rules, fee, and date, signed by both sides.
02

Pilot the hardest chain

We ask your team one question: which job chain would you least like us to touch? That chain goes first, in full, on AWS, with your production data. If the architecture is going to crack, it cracks here, when fixing it costs days rather than months.

Stage exit

Pilot results reviewed with you and architecture decisions closed.
03

Migrate in lineage waves

Wave boundaries come from lineage. Any job whose inputs are still produced on the legacy side waits for a later wave, and the shared pieces, reference tables and control flags, are kept in sync across both estates for as long as both are live.

Stage exit

A wave counts as accepted when it has run clean in your AWS account for the agreed number of cycles and your team says so.
04

Reconcile

Every day’s inputs flow through both estates, and the comparison harness publishes its results each morning: matches, variances, finish times. The contract fixes the last day of this stage, so reconciliation has a deadline like everything else.

Stage exit

Data owners sign the parity ledger.
05

Retire the legacy estate

Switching a wave is the least dramatic part of the program, because it has been practiced at production volume and the conditions for falling back are already written down. Once the final wave holds, three things happen in order: the scheduler stops, the servers go, and the license renewals are cancelled.

Stage exit

Your sign-off on each go-live, and on the final decommission checklist.

How many waves? That falls out of the dependency graph, not a template. A clean estate of a few hundred jobs sometimes moves in one window; most need several. Waves are always cut along lineage, never by team or by tool, because team and tool boundaries are exactly where a forgotten control table hides.

Prove it on your worst job

Pick the chain your team dreads. We'll move it first, on your data.

Everything after that is planned from evidence, not estimates.

See how we work

How Is Data Reconciliation Done When Pipelines Move to AWS?

A harness feeds identical inputs to the legacy and AWS pipelines and compares the results at five levels: rows and keys, column checksums, business totals, file formats, and timing. Each mismatch is fixed, explained, or formally accepted. The rules, the tolerances, and who signs are contract terms, agreed before conversion starts.

Ask a vendor how they'll know the new pipelines are right. If the answer is "testing," ask how testing ends. Our answer is a ledger: one row per dataset, with the comparison, the tolerance, and a named signer, written during discovery for every engagement of our ETL modernization services. When every row is signed, testing is over, and not before. Our application modernization services hold application code to an equivalent bar.

Comparison

Method

Signer

Row and key completeness

Row counts and primary-key sets diffed per table and partition, every cycle

Data owner for the table

Content equality

Column hashes compared across both outputs, with a pre-agreed rounding allowance for floating-point sums

Data owner for the table

Business totals

Revenue, units, balances, and other control figures matched to the cent

Finance or controllership lead

Slowly changing history

Type 2 versions and effective dates compared, not only current rows

BI or analytics lead

Outbound files and feeds

Layout, encoding, delimiters, and partner schemas compared byte for byte

Owner of each feed

Batch window and freshness

Finish time of each critical chain and latency of each stream, against today's numbers and the target

Platform or operations lead

Failure behavior

Late, empty, and malformed inputs injected on purpose to test alerting and restarts

On-call owner

"This is the phase that turns a migration from an act of faith into a reconciliation you can read."

Mactores, How We Work

That last row deserves a word. A pipeline that matches perfectly on a quiet Tuesday can still fail the first time a partner file arrives late or half-empty, so we break things deliberately during the parallel run and check that the new estate notices, alerts the right person, and recovers without a manual rerun.

What does a pipeline cutover window look like?

On the night itself, the work is mostly watching. The steps ran in rehearsal, the harness does the comparing, and the only open question is whether each check comes back green. If one doesn't, the owner of that checkpoint on your side decides, with the results on screen and an FDE beside them.

Freeze: final legacy outputs captured as the reference
Compare: the first AWS run checked against that reference
Repoint: consumers moved in the groups set during planning
Time: chain finish times logged against the window target
Hold: legacy scheduler paused, never deleted, through the fallback period

See your parity rules in draft

We'll draft the comparison rules for one of your real job chains.

Every rule names a dataset, a tolerance, and the person who signs.

Request the parity rules →

What Should an Audit-Ready ETL Migration Change About Access and Lineage?

It should close the gaps legacy ETL accumulated over the years: passwords in parameter files, service accounts that can reach everything, production data sitting in test, and figures nobody can trace. On AWS each gap has a specific fix, and the program's own parity records become audit evidence as a side effect.

Legacy ETL servers are among the most privileged machines in any estate. They hold credentials to nearly every source system, often in plain-text parameter files, and they've been allowed to write almost anywhere for a decade. A migration is the natural moment to fix that, and also a risky one, because until the legacy side is switched off, every secret and every sensitive table exists twice.

Connection secrets moved out of parameter files and package configs into AWS Secrets Manager, rotated, and scoped per job
An IAM role per job that can touch only the sources and targets that job needs, with AWS CloudTrail recording each call
Table, column, and row permissions on the lake and warehouse defined in AWS Lake Formation as part of the target design
No production PII or PHI in development, where test runs use masked or synthetic data instead
Every published figure traceable to its source columns, through lineage captured during conversion
Pipeline code, configuration, and schedules held in version control, with every production change reviewed

PHI raises the stakes on all six. Our healthcare data platform work for payers and providers is where these controls run against live claims and member data every day, at production scale, under real audit scrutiny.

Framework

What the program must protect

SOX

Evidence of approved changes for every job that touches the close

HIPAA

PHI exposure kept to the minimum each feed needs

PCI DSS

Tokenized card data that stays tokenized from source to target

FFIEC guidance and SEC rules

Lineage strong enough to reproduce any reported figure on request

SOC 2

How Mactores' own team is governed while it holds access to your systems

GDPR

Where EU and UK personal data is stored and processed, with dev and test environments in scope too

DORA

Third-party ICT oversight and resilience testing across EU financial data flows

These frameworks shape the design. They aren't certifications Mactores claims, and no listed body endorses us. Examiners at banks and insurers tend to ask the same question in different words: show me where this number came from. Our financial services data modernization practice is built around answering it.

Governance is also moving into the pipeline itself. Gartner predicts that by 2030, half of organizations will use autonomous AI agents to turn governance policies into machine-verifiable data contracts. Pipelines rebuilt with explicit schemas, named owners, and tests are ready for that shift. A decade of undocumented mappings isn't.

Turn a finding into a deliverable

Plain-text credentials? Missing lineage? Unreviewed job changes?

Share the audit note, and we'll show where in the program it gets fixed.

Walk through my audit scope →

What Does ETL Modernization Look Like in Each Industry?

The sector mostly changes which feeds go first and who signs for them. A bank's first wave stays away from the close; a payer's first wave proves PHI handling; a software company moves internal events before product analytics; a manufacturer starts away from the line; a broadcaster plans around its event calendar.

Financial Services

Pipelines that reconcile before the examiner asks.

The first feeds to move are rarely the regulatory ones; those wait for a window well clear of quarter-end. When they do move, the parallel run has to show identical control figures cycle after cycle, and each regulatory number gets a lineage document tracing every hop from source to report. Intraday trade and market-data feeds are often the earliest candidates for change data capture, with latency targets the front office signs.

Financial services modernization

Healthcare & Life Sciences

PHI that never takes a shortcut.

Every stage assumes PHI is present: masked data for testing, job roles that see only the fields they process, and audit logging from the first run. HL7 and FHIR interfaces keep their message contracts unchanged through cutover, and where FHIR resources are central, the target design adds Amazon HealthLake. Files sent to clearinghouses and trading partners are diffed against their legacy versions before the switch, because a broken eligibility file turns into denied claims a week later.

Healthcare modernization

Internet & Software

Event data that never blocks a deploy.

Release trains set the calendar in this sector. Product-event, billing, and customer 360 pipelines switch on days your teams aren't shipping, and nightly jobs behind user-facing features are the first to become streams. Data contracts sit between product schemas and analytics, so a renamed field fails a check instead of a dashboard. Analytics your customers see inside your product are reconciled first and held to exact-match rules.

Internet & Software modernization

Manufacturing

Factory data that keeps flowing through the switch.

Plants rarely have a quiet hour, so the plan bends around production, not the other way round. Historian, MES, quality, and supplier data paths are traced out of the OT network early, and each plant switches in a window its own production planner picks. Supplier EDI runs through the new pipelines in shadow mode first, and every line-side job gets a site-level fallback that the plant can trigger itself.

Manufacturing modernization

Telco, Media, Entertainment, Gaming, and Sports (TMEGS)

Pipelines built for the night everyone tunes in.

Telemetry from players, viewers, and ad servers is spiky by nature: a tied match or a surprise release can multiply volume within minutes. Amazon Kinesis or Amazon MSK capacity is load-tested against last season's worst night before anything switches, and the cutover calendar is built outward from your event schedule. Royalty and rights feeds, where one lost record becomes a payment dispute, are reconciled record by record.

TMEGS modernization

If you're comparing this page with how other firms treat your sector, look at the first wave. That's where industry knowledge shows up in a plan, and it's the one sequencing decision we make with your business calendar open and your feed owners in the room, before any wave is scheduled.

Start where the risk is

Which feed would hurt most if it were wrong for a day?

Tell us, and we'll design wave one around protecting it from day one.

See all verticals

How Much Do ETL Modernization Services Cost, and Who Pays If the Date Slips?

There's no price list, because no two estates cost the same. A fee is quoted once discovery has measured five things: job count and logic depth, the mix of source tools, how fresh the data must be, regulatory scope, and how many consumers depend on outputs. Then it's fixed; slips we cause are ours.

Hourly billing for data pipeline services has an odd consequence: the worse the discovery, the larger the invoice. Our fee is fixed once discovery ends, so a thorough discovery is in our interest as much as yours.

Pricing disclaimer

Planning information only. Mactores quotes no fee before discovery.

Driver

Heavier when

Job count and logic complexity

There are more jobs, deeper chains, custom code inside the ETL tool, and business rules divided between the ETL tool and stored procedures

Source-tool mix

Several ETL tools run at once, use vendor-specific languages, or hold generated code that someone hand-edited

Freshness targets

Feeds move from nightly batch to minutes, or onto change data capture and streaming

Regulated scope

Feeds carry PHI or card data, or examiners expect figure-level lineage

Downstream reach

Outputs feed many dashboards, partner deliveries, extracts, and models

Every one of these can be counted from artifacts you already have, so the quote rests on your repository and logs rather than on a workshop's guesses.

How do companies fund an ETL migration?

Look at last year's invoices. The ETL license renewal, the support uplift for an aging version, two sets of servers (one for disaster recovery), and the on-call overtime for failed nights are where most programs find their funding, and every one of those stops once the old estate does. A March 2026 benchmark of 500 senior data and technology leaders found data teams spend 53 percent of their time maintaining pipelines, and put the business cost of data downtime at about $49,600 an hour. A rebuilt, observable estate gives much of that time back.

The landing zone has also become cheaper to run. AWS Glue 6.0 arrived with a 30 percent price reduction and full Apache Iceberg v3 support, which changes the run-cost math for anyone who priced AWS Glue ETL a few years ago. One more line item becomes possible afterward: the agents built on top of the new pipelines, which tend to be why the business wanted fresher data in the first place.

Spending today

Where it goes after cutover

Per-core or per-node ETL license renewals

The conversion and parity work of this program

Paid extended support on versions past end of support

Pay-per-use Glue and EMR capacity matched to actual job runs

ETL servers, grid nodes, and their DR mirrors

Managed orchestration with no servers to patch

Hours spent rerunning failed jobs and chasing late files

Alerts routed to owners before a dashboard goes stale

Duplicate extracts built by teams who couldn't wait

Shared, tested modules every team reads from

Hadoop hardware sized for one busy night a month

Capacity that scales with each night's actual workload

Ask your AWS account team about the Migration Acceleration Program as well. The assessment Mactores produces in discovery is built to the level of detail MAP reviews call for, though the funding decision is always AWS's.

Count what the old estate costs

License, support, servers, and failed-night overtime, added up.

We'll set that total beside a fixed fee and a date.

Book a scoping call →

Which ETL and Pipeline Terms Should You Understand Before Comparing Proposals?

If a proposal uses these nine terms without numbers beside them, risk is hiding there: job lineage, batch window, change data capture, ELT, zero-ETL, orchestration, output parity, schema drift, and backfill. The table explains each one and how it changes effort, run cost, or the schedule of an ETL program.

Term

What it means

Effect on your program

Agent-native

How Mactores is built: agent labor covers the volume of a program, and people own its judgment calls

Turns most of the program's hours into a predictable cost, so a fixed fee needs no hidden padding

FDE

A named Mactores specialist placed inside your data team for the whole program

You never brief a replacement team halfway through

ETL

Extract, transform, load: data is reshaped on a separate server before it reaches the target

Logic hidden inside the tool is the main conversion effort

ELT

Extract, load, transform: raw data lands first and is reshaped inside the warehouse or lake

Often simpler to operate, but it shifts cost into warehouse compute

Job lineage

The chain from each source field, through every job, to the report or feed that uses it

It decides wave order, and gaps in it are where cutovers break

Batch window

The hours nightly jobs have to finish before people need the data

Tight windows force redesign, not just conversion

Change data capture (CDC)

Reading changes from a source database's log instead of re-extracting whole tables

Cuts load and latency, but adds a stream someone has to operate

Zero-ETL

AWS-managed replication from operational databases into analytics targets, with no pipeline code

Retires extract jobs outright, provided no hidden logic depended on them

Orchestration

The scheduling layer that runs jobs in order and handles retries, such as Amazon MWAA or AWS Step Functions

Hidden dependencies surface here, so it sets the pace of each wave

Output parity

The written, per-dataset standard for "the same" between old and new pipelines

Without it, nobody can say when testing is finished

Schema drift

Upstream changes to columns or types that break, or silently corrupt, downstream jobs

Contracts and alerts stop it before it reaches a report

Backfill

Re-running a pipeline over past periods to rebuild its outputs

Deep history multiplies run time and cost during the migration

Numbers, not adjectives

Our contracts attach a value to these terms: a tolerance, a window, a date.

The commitment page shows how they're enforced.

See the contract terms →

How Can Mactores Help You Out?

Three things, in order. A wave plan within five business days of discovery starting. A fixed fee and a contracted cutover date at the end of discovery, owned by FDEs you've already met. And, on that date, a legacy ETL estate that is actually switched off. That last one is where most programs fall short.

Our FDEs build agents as well as pipelines, and it changes how they design the pipelines. Lineage gets captured because an agent will be asked to cite its source. Freshness targets get set because an agent acting on yesterday's inventory makes expensive mistakes. Contracts get written because an agent can't tell a renamed column from a missing one.

Gartner found that organizations with successful AI initiatives invest up to four times more in data foundations such as quality and governance than those reporting poor AI outcomes. Pipelines are the part of that foundation that runs every night, and no model can compensate for a feed that arrives late or wrong.

When you shortlist ETL migration companies, request a sample parity ledger and a decommission record from a finished program, and hold us to the same request. A firm that can't produce both is selling conversion, not migration.

There's usually a second chapter, and it tends to be about agents. Our generative AI consulting team builds them for production, and pipelines that already carry lineage and data contracts are exactly the foundation that work needs.

ETL estate assessment

You get
A job register with lineage, a disposition per job, and the evidence pack AWS reviews for MAP

Choose it when

You're building next year's budget and need real numbers

Migration rescue

You get
What already converted, kept; what didn't, re-planned; parity rules and a new contracted date

Choose it when

A previous attempt has been "nearly done" for months

Hardest-chain pilot

You get
One job chain, chosen by you for its difficulty, live on AWS by a contracted date

Choose it when

The board wants a result before it approves the full program

"For well-defined scopes, we offer fixed-price engagements that transfer timeline and budget risk to us."

Mactores, Our Commitment

In every pipeline contract:

Scope, fee, and cutover date tied together, so only a scope change moves the other two
Our delays, our cost
Parity rules signed per dataset before any job converts
Legacy decommissioning written in as a deliverable with its own date
Named FDEs, with the same names at the end as at the start

Which AWS validations cover Glue, EMR, and streaming work?

Credentials are where you shouldn't take our word for anything, so here are the ones AWS grants after reviewing delivered customer work.

Mactores holds AWS Premier Tier Services status and the AWS Agentic AI Specialization. Of its seventeen AWS Service Validations, five map directly to pipeline work: AWS Glue, Amazon EMR, Amazon Kinesis, Amazon MSK, and AWS Database Migration Service. The Data and Analytics competency, one of the seven Mactores holds, covers this service. Our AWS partnership page has the full set, and AWS Partner Solutions Finder lists them publicly as well.

Tier: AWS Premier Tier Services
AI specialization: Agentic AI
Competency: Data and Analytics (1 of 7)
Validations on the pipeline path: AWS Glue · Amazon EMR · Amazon Kinesis · Amazon MSK · AWS DMS (5 of 17)
Customer go-lives on AWS: Since 2008
Delivery against committed dates: 94% on time or early

7 Consulting Competencies

Skip the RFP

Tell us the tool, the rough size of the estate, and what's forcing the timing.

We'll answer the same business day or the next.

Talk to us →

What Are the Most Common Questions About ETL Modernization?

What are ETL modernization services?

They replace aging extract, transform, and load tooling, such as Informatica, SSIS, DataStage, or Hadoop, with pipelines on a current platform. In practice that means three outcomes: the old jobs stop running, the new ones produce matching outputs, and the people who maintain them can read the code. Mactores delivers all three on AWS.

How long does an ETL migration take?

Discovery answers that precisely, and the answer becomes a contract date. Until then, a useful reference point is Mactores' median time to production across all engagements: twelve weeks. Estates with several ETL tools and thousands of jobs run longer, in waves, with early waves live while later ones are still converting.

How do you migrate Informatica PowerCenter to AWS Glue?

Agents parse the PowerCenter repository exports, including mappings, mapplets, sessions, and parameter files, and convert the logic to Spark on AWS Glue or to SQL on Amazon Redshift. FDEs rewrite custom transformations and lookup behavior that don't carry over directly. Every converted mapping has to match its PowerCenter output before it switches.

What are the alternatives to Informatica PowerCenter on AWS?

For most workloads, AWS Glue for Apache Spark handles transformation, Amazon MWAA or AWS Step Functions handles orchestration, and Amazon Redshift or Apache Iceberg tables on Amazon S3 hold the results. Some teams move to Informatica's own cloud service instead. The right choice depends on licensing, skills, and how much logic you'd rather keep in a tool than in code.

Can Mactores commit to a fixed cutover date?

Yes, and the contract says what happens if it moves. The rule follows the cause: a delay Mactores creates is absorbed by Mactores, while one created by missing access, environments, or decisions on your side is billed hourly, at a rate you'll have seen before signing.

How much do ETL modernization services cost?

Each program gets a single fixed fee, set at the end of discovery. Before that, any number would be a guess, because a job count says little about custom code, tool mix, freshness needs, or audit scope. Discovery delivers a wave plan within five business days, and the fee follows when it closes.

ETL vs ELT: which should our rebuilt pipelines use?

Usually a mix. ELT works well when raw data can land in Amazon S3 or Redshift and be shaped there with SQL. ETL remains the better choice when data must be cleansed, masked, or filtered before it lands, which is common for regulated fields. Many estates end up with ELT for analytics and ETL wherever regulated data is involved.

Do we need real-time pipelines, or is batch good enough?

Batch is fine for most reporting. Streaming or change data capture earns its cost where a decision depends on freshness: fraud checks, inventory, pricing, operational alerts, and AI agents acting on current data. We set a latency target per feed, and only the feeds that genuinely need minutes get a stream.

What if the business changes a job while the migration is underway?

It happens on every program, so the process is set up front. Changes to in-flight jobs go through one queue, get applied to the legacy and AWS versions together, and receive a fresh parity check. Changes that add scope get a change order with their own price and date impact, agreed before the work starts.

Is agentic AI ready for legacy ETL modernization?

For the repetitive parts, yes. Agents handle parsing thousands of mappings, tracing lineage, drafting converted code, generating tests, and running comparisons very well. Deciding what to retire, how to sequence waves, or whether a variance is acceptable needs a person who will answer for the result, so at Mactores those calls belong to FDEs.

Can the rebuilt pipelines load into Snowflake or Databricks on AWS?

Yes. Most programs land on AWS-native services such as AWS Glue, Amazon EMR, and Amazon Redshift, but if your analytics already run on Snowflake or Databricks inside AWS, the rebuilt pipelines can load there instead. The parity rules and the contract terms stay the same either way.

Will our own team be able to run the new pipelines?

Yes, and that's part of the deliverable. Code lives in your repository, runbooks cover every critical chain, alerts route to named owners, and your team runs the parallel period alongside our FDEs before cutover. Everything runs on AWS services and your own code, so there's no Mactores dependency after handover.

Which Deadline Is Your ETL Estate Up Against?

Most ETL programs start because a calendar forced them to. If yours already has a date on it, an FDE can test that date against your job list in about an hour and tell you plainly whether it holds, what it would take, and which chain we'd move before any other.

Book a scoping call Email lets-talk@mactores.com

01
Start with the reason. What's forcing the move, and which tool it affects. A short email works.
02
Get a named FDE. By the next business day, a delivery lead puts an FDE from the delivery team on a call with you.
03
Review the plan. Five business days into discovery, you see the waves and the draft parity rules, and you know what a fixed-fee contract would cover.

Data Pipeline & ETL Modernization Services

Top Mactores Clients

Why do legacy ETL migrations stall halfway?

Can an ETL Migration Really Be Delivered on a Fixed Date?

Which Jobs, Tools, and Deliverables Do Data Pipeline Modernization Services Cover?

Is this a tool swap or a pipeline rebuild?

Job, schedule, and lineage discovery

Logic conversion with readable output

Orchestration and scheduling rebuilt

Streaming and change data capture where freshness pays

Output-parity testing and cutover

Observability, handover, and legacy retirement

How does each legacy ETL tool change the migration plan?

Do All Legacy ETL Jobs Need to Be Rewritten for AWS?

Where do zero-ETL and ELT fit?

What Are the Stages of an ETL Migration, and Who Signs Off on Each?

Discover

Pilot the hardest chain

Migrate in lineage waves

Reconcile

Retire the legacy estate

How Is Data Reconciliation Done When Pipelines Move to AWS?

What does a pipeline cutover window look like?

Where Has Mactores Moved Live Data Pipelines Without Disrupting the Business?

A factory's scattered systems, streamed into one command center (HP)

Design workloads routed through a data lake, not a queue (Synaptics)

A Hadoop-era data foundation retired under live load (Flipboard)

Every dependency mapped before anything moved (Poshmark)

What Should an Audit-Ready ETL Migration Change About Access and Lineage?

What Does ETL Modernization Look Like in Each Industry?

Financial Services

Healthcare & Life Sciences

Internet & Software

Manufacturing

Telco, Media, Entertainment, Gaming, and Sports (TMEGS)

How Much Do ETL Modernization Services Cost, and Who Pays If the Date Slips?

How do companies fund an ETL migration?

Which ETL and Pipeline Terms Should You Understand Before Comparing Proposals?

How Can Mactores Help You Out?

ETL estate assessment

Migration rescue

Hardest-chain pilot

Which AWS validations cover Glue, EMR, and streaming work?

What Are the Most Common Questions About ETL Modernization?

Which Deadline Is Your ETL Estate Up Against?