Open Data Lakehouse: Modern AI Architecture
Key Points
- Enterprises face exploding data volumes, diverse workloads, and costly, siloed architectures that make traditional data warehouses and data lakes inadequate for modern AI and ML use cases.
- To scale AI, organizations need to modernize inefficient data architectures, unify access across hybrid‑cloud sources, and accelerate insights with built‑in governance and automation.
- IBM’s Watson X Data implements an open data lake‑house that adds a shared Iceberg/OpenTable metadata layer on top of cost‑effective storage (Parquet, Avro) to eliminate data duplication and simplify cross‑engine analytics.
- The platform combines IBM‑enhanced Presto, customized Spark libraries, and optional registration of existing warehouses (e.g., Db2, Netezza) to deliver high‑performance, low‑cost analytics, promising up to 50 % lower spend than classic data warehouses.
Sections
Full Transcript
# Open Data Lakehouse: Modern AI Architecture **Source:** [https://www.youtube.com/watch?v=hB6olelYhr0](https://www.youtube.com/watch?v=hB6olelYhr0) **Duration:** 00:05:17 ## Summary - Enterprises face exploding data volumes, diverse workloads, and costly, siloed architectures that make traditional data warehouses and data lakes inadequate for modern AI and ML use cases. - To scale AI, organizations need to modernize inefficient data architectures, unify access across hybrid‑cloud sources, and accelerate insights with built‑in governance and automation. - IBM’s Watson X Data implements an open data lake‑house that adds a shared Iceberg/OpenTable metadata layer on top of cost‑effective storage (Parquet, Avro) to eliminate data duplication and simplify cross‑engine analytics. - The platform combines IBM‑enhanced Presto, customized Spark libraries, and optional registration of existing warehouses (e.g., Db2, Netezza) to deliver high‑performance, low‑cost analytics, promising up to 50 % lower spend than classic data warehouses. ## Sections - [00:00:00](https://www.youtube.com/watch?v=hB6olelYhr0&t=0s) **Untitled Section** - ## Full Transcript
an open data lake house you've heard the
term but what does it mean for modern
data management let's start with how we
got here there are key challenges that
have led to the open data lake house
concept including how the amount types
and cost of managing data has exploded
how data needs to be managed differently
across environments and how consumption
of data has changed with new use cases
driven by Ai and when we observe how
Enterprises are dealing with the
explosion management and consumption of
data we find most leverage multiple
siled and monolithic data warehouses and
data lakes on-prem and in the cloud
warehouses typically offer high
performance for processing terabytes of
structured data for business
intelligence use cases but can quickly
become unsuitable and expensive for data
engineering machine learning and
evolving AI workloads price to
Performance is less than optimal by
running these types of workloads in a
data warehouse
data Lakes offer lower cost solutions
for Big unstructured Data but can often
become data swamps and challenged with
extracting insights in a highly
performant and simplified way these data
management challenges inform three
critical needs for Enterprises to scale
AI first is to modernize ineffective
data architectures and reduce data
warehousing costs
second is to seamlessly access and unify
data across disparate hybrid Cloud
sources
and third is to accelerate time to
Insight with built-in governance and
automation the solution is a data lake
house that helps you optimize for both
cost and performance by leveraging
cost-effective repositories combined
with multiple fit for purpose engines to
power various analytics and AI use cases
Watson X data gets to decisions you can
trust in minutes for up to 50 percent
less than the cost of a data warehouse
so how does it work let's take a look at
how Watson X data is architected Watson
X data starts with your data wherever it
resides it provides the foundation to
connect to your existing data lakes and
databases and combine them with new data
to develop and unlock new use cases and
new insights open data formats such as
parquet and Avro are all supported we
then introduce a consistent and shared
metadata layer which organizes data in
the iceberg OpenTable format this allows
different engines to access and power
all analytic workloads while sharing a
single copy of data what does this mean
we eliminate data silos and duplication
of data while reducing cost and
governance risks the last layer brings
multiple query engines this layer is at
the heart of IBM's open data lake house
Watson X data brings together the best
of IBM and the best of Open Source it is
built on IBM's enhanced Presto engine
and leverages Spark with customized
libraries for fast reliable and
efficient processing at scale
you can also register existing data
warehouse engines such as IBM db2 IBM
natiza performance server or another
data warehouse that supports Iceberg to
Watson X data what could make it better
Watson X data can be deployed as
containerized software or as a fully
managed service in multiple public
clouds
with flexible deployment options you can
access all of your data and maximize
workload coverage across all hybrid
Cloud environments leveraging our
multi-engine architecture organizations
now have the opportunity to reduce the
cost of their data warehouse by up to 50
percent through workload optimization
non-optimal costly Warehouse workloads
can now be shifted to lower cost storage
compute and fit for purpose query
engines that dynamically scale up and
down Watson X data also helps
organizations get value out of
ineffective data Lakes by leveraging a
new generation of SQL engines designed
to bring warehouse-like performance and
functionality over Big Data the
Simplicity of what's the next data
allows users to connect to any storage
and analytics environment in minutes and
start generating trusted insights
finally Watson X data complements your
existing data and AI Investments by
integrating into your organization's
data fabric architecture the use of
Industry standards means IBM can
interoperate seamlessly across multiple
data ecosystems our solution integrates
with both IBM and third-party data
science business intelligence and data
integration tools for example what's the
next data integrates with IBM's
governance capabilities to enable
responsible transparent and explainable
data and AI workflows across the
Enterprise IBM Watson X data is the
foundation to help your organization
scale analytics and accelerate the
adoption of AI thank you if you like
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