Unify Data Engineering, BI, and ML on the Lakehouse
Databricks by AMSYS delivers a unified Lakehouse platform with collaborative notebooks, managed Delta Lake, and MLflow integration. AMSYS architects, implements, and optimizes your Databricks environment to drive data‑driven innovation at enterprise scale.
What is Databricks Lakehouse?
Databricks Lakehouse combines the best of data warehouses and data lakes into a single platform. AMSYS leverages Workspace, Delta Lake, MLflow, and Unity Catalog to build end‑to‑end pipelines for batch, streaming, BI, and machine learning workflows.
Break down silos, speed up analytics, and scale ML with AMSYS expertise.
Data scattered across lakes, warehouses, and marts hinders insights. AMSYS builds Lakehouse architectures that unify all data in Delta format.
Traditional warehouses struggle with concurrency and large volumes. AMSYS optimizes Databricks SQL for sub‑second query performance at scale.
Hand‑coded pipelines are brittle and costly. AMSYS uses Delta Live Tables and modular notebooks to simplify ETL/ELT development.
Disjointed tools slow model development and deployment. AMSYS integrates MLflow and Feature Store for repeatable, production‑ready MLops.
Leverage Lakehouse capabilities for agility, performance, and governance.
ACID transactions, schema enforcement, and time travel ensure data integrity and auditability.
Shared Python, SQL, R, and Scala workspaces with version control and real‑time co‑authoring.
Track experiments, manage models, and automate deployments with a unified MLops framework.
High‑concurrency SQL endpoints and dashboards for BI teams, powered by Photon engine.
Centralized metadata, lineage, and fine‑grained access control across all Databricks assets.
Accelerate insights, reduce costs, and scale AI initiatives.
Proven methodology to deploy, optimize, and support your Lakehouse.
Evaluate your current data estate and define a Lakehouse transformation plan aligned with business goals.
Design secure, multi‑cloud or on‑prem Databricks workspaces with network isolation and infrastructure as code.
Build modular ETL/ELT with Delta Live Tables, notebooks, and jobs following best practices.
Implement MLflow, Feature Store, and model serving pipelines for reproducible, monitored ML lifecycle.
24/7 AMSYS support, performance tuning, and continuous optimization to keep your Lakehouse performant.
Guidelines to maximize performance, security, and maintainability.
Partition and optimize tables for file size, data skipping, and Z‑Order indexing.
Use modular, parameterized notebooks and repos for versioned development.
Automate cluster policies, instance types, and auto‑termination to control costs.
Standardize experiment names, tags, and metrics to enable team‑wide reproducibility.
Define metastore hierarchy, enforce permissions, and enable cross‑workspace data sharing.
Simplify and scale your data landing zones.
Incrementally ingest files from cloud storage with schema inference and watermarking.
Build declarative, managed pipelines with automatic error handling and monitoring.
Native hubs for Kafka, Event Hubs, and Kinesis enable high‑throughput event ingestion.
Process and transform data with reliability and speed.
Leverage next‑gen query engine for up to 3× faster SQL performance on large datasets.
Tune Spark configurations automatically for best throughput with AMSYS autotuning scripts.
Automate compaction, optimize writes, and manage file layout for consistent performance.
Use SQL endpoints, dashboards, and alerts to empower self‑service BI at scale.
Develop, train, and deploy models with MLflow.
Create and share feature definitions for consistent model inputs across teams.
Promote models through Staging, Production, and Archived stages with approvals.
Deploy models as REST endpoints and monitor performance and drift in real time.
Ensure security, compliance, and discoverability.
Unity Catalog
Centralized metastore with table, schema, and data lineage across workspaces.
Access Controls
Fine‑grained permissions on tables, views, and notebooks via IAM and Unity Catalog policies.
Lineage Tracking
Visualize data flow from ingestion through transformation to consumption for audit readiness.
Data Discovery
Tag, classify, and search assets using Unity Catalog’s built‑in data catalog features.
