Unlocking Enterprise Data Lakehouse: Trends, Challenges, and Volcano Engine EMR Solutions

Data Lakehouse Open‑Source Trends

This article, compiled from the fourth session of the Volcano Engine Developer Community Technical Lecture, introduces the open‑source trends of data lakehouses, the architecture and characteristics of Volcano Engine EMR, and how to build enterprise‑grade data lakehouses using EMR.

Trend 1: Architecture Shifts Toward LakeHouse

LakeHouse combines the flexibility of a DataLake with the transactional guarantees of a Data Warehouse. It introduces a Table Format storage standard with four key features:

ACID support and historical snapshots to ensure safe concurrent access and enable use cases such as streaming and AI.

Multi‑engine access compatible with Spark, Presto, Flink, etc.

Open storage that works across local, HDFS, and cloud object stores.

Table format definition that stores both data and metadata on the underlying storage.

The three main implementations are Delta Lake , Iceberg , and Hudi . Although their design goals differ slightly, they share transactional and streaming support, leading to increasingly similar architectures.

However, Table Format is not a silver bullet. Limitations include sub‑optimal write performance for streaming, maintenance overhead, ecosystem gaps, and limited business appeal. Commercial solutions such as Databricks (Delta Lake), Tabular (Iceberg), and OneHouse (Hudi) provide higher‑level services that mitigate these issues.

Trend 2: Fine‑Grained Memory Management and Efficient Execution

As engines mature, performance demands drive a shift toward native execution and vectorization. Notable examples:

Spark’s Photon project promises up to 2× acceleration on TPC‑DS benchmarks.

Presto’s Velox native engine claims a 3× performance boost.

ClickHouse and Doris also demonstrate native‑engine characteristics.

Vectorization, pioneered by Hyper (codegen) and MonetDB, processes batches of rows simultaneously, leveraging CPU SIMD and pipeline execution for higher throughput compared to row‑wise processing.

Trend 3: Multi‑Modal Compute

Engines are expanding beyond their original domains: Spark now supports streaming and AI, Trino adds batch capabilities, Flink incorporates batch and AI, and Doris enhances multi‑catalog support. This convergence leads to a few dominant engines for similar workloads, while niche scenarios remain fragmented.

Trend 4: Real‑Time Analytics

Analytics are moving toward real‑time processing. Near‑real‑time OLAP engines include ClickHouse, Doris, and Druid, while streaming engines encompass Flink, Kafka SQL, and emerging streaming databases such as Materialize and RisingWave.

Enterprise Challenges in Building Data Lakehouses

Enterprises face five major challenges:

Complex end‑to‑end data pipelines : Even a small app requires data back‑fill, CDC, log ingestion, and ETL.

Diverse lakehouse requirements : Machine‑learning workloads demand feature engineering, batch, streaming, and interactive analytics.

Broad data sources : Transactional data, asset data, user behavior, and intermediate data.

Multiple stakeholder roles : Managers, business users, developers, and infrastructure teams.

Need for platform‑specific extensions : Customization for business needs, productization, and platformization.

These challenges manifest as stability, scalability, functionality, performance, cost, operations, security, and ecosystem concerns. Volcano Engine EMR addresses these comprehensively.

Volcano Engine EMR Overview

Volcano Engine EMR is an open‑source big‑data platform (E‑MapReduce) that integrates Hadoop, Spark, Flink, Hive, Presto, Kafka, ClickHouse, Hudi, Iceberg, and more. It offers 100% compatibility with community versions, a semi‑managed white‑box environment, and supports real‑time lake, warehouse, and unified lake‑warehouse architectures.

Open‑source compatibility & open environment : Full compatibility with mainstream community releases and a semi‑managed white‑box setup.

Enterprise‑grade engine optimizations : Optimizations and security features for Spark, Flink, and other core engines.

Stateless cloud‑native lakehouse : Decouples state from compute, enabling elastic scaling.

Cloud‑native easy operations : One‑click cluster creation/destruction, fine‑grained monitoring, and alerting.

Stateless and Transient Clusters

Stateless design externalizes all stateful components (history server, metadata services, audit logs, intermediate data), allowing compute clusters to be truly stateless and elastically scalable.

Hive Metastore Service

A centralized metadata service that can be shared or dedicated per department, reducing cost while supporting high‑availability use cases.

Persistent History Server

Externalized history services for YARN, Spark, Flink, and Presto provide independent, long‑living storage of job histories (retained 60 days) and a native UI with IAM SSO integration.

Storage Separation and Elastic Scaling

EMR uses CloudFS (HDFS‑compatible layer on top of TOS object storage) and TOS for cold data. CloudFS provides caching for hot data, while CloudFS’s HDFS semantics simplify integration with open‑source components.

Cloud‑Hosted Easy Operations

The management console offers cluster, service, node, log, configuration, permission, and auto‑scaling controls. Users can quickly spin up minimal clusters (e.g., https://www.volcengine.com/product/emr) and monitor resources, anomalies, and job diagnostics.

User‑Friendly Experience

EMR provides a unified job management UI with global resource views, one‑click job details, diagnostics, and future plans for automated optimization suggestions.

Future Roadmap for EMR Lakehouse

Data acceleration : Enhance cache layers (file‑level, page‑level) and indexing (bitmap, Bloom filter) to offset object‑storage performance gaps.

Address core pain points : Solve CDC analysis and multi‑path lakehouse fragmentation; enable Doris to directly access Hive/Iceberg/Hudi tables for seamless lake‑warehouse interaction.

Open‑source contributions : Integrate Alluxio data block features, improve Doris multi‑catalog and hot‑cold separation, and add secondary indexes to Iceberg.

AI4Data (Intelligent Data Steward) : Auto‑diagnose costly SQL/jobs, automatically optimize queries using data distribution, cache, indexes, materialized views, and provide smart ops such as auto‑scaling, failover, and data balancing.

Product polishing : Strengthen the product foundation, adopt cautious rollout of new features, and refine cluster creation, elastic scaling, job development, and ecosystem integration.

Q&A

Q: What is the learning cost, ease of use, compatibility, and performance of Volcano Engine EMR? A: EMR is 100% compatible with open‑source versions, resulting in zero learning cost for the engine; the interactive console is intuitive, and extensive engine‑side optimizations deliver strong performance.

Q: Which industries and scenarios can EMR serve? A: As a comprehensive big‑data stack, EMR covers batch, streaming, interactive analytics, and machine‑learning across all industries.

Q: Does EMR solve the small‑file, performance, and streaming update issues of current lakehouse technologies? A: While the small‑file problem is inherent to table formats, EMR adds internal features to mitigate usage‑level challenges.

Q: What are the trade‑offs between storage‑compute separation and integrated OLAP engines like ClickHouse and Doris? A: Choose Doris for extreme analytical performance; choose Presto for cost‑effective storage‑compute separation. Consider workload, cost, and elasticity when selecting.