Hadoop transformed data processing by letting companies process large amounts of data on their data centers’ commodity hardware. However, the open-source framework does not meet modern enterprise computing needs. This article will review the Hadoop ecosystem and explain how a modernized open data lakehouse future-proofs enterprise data strategies.
Core Components of the Hadoop Ecosystem
Hadoop’s four modules — Core, HDFS, MapReduce, and YARN — are central to the overall ecosystem.
Hadoop Common
Hadoop Common is a set of shared resources, such as compression codecs and native IO utilities, used by the other three modules.
Hadoop Distributed File System (HDFS)
The Hadoop distributed file system is a data storage framework that writes large datasets across multiple machines to ensure high throughput and resilience. Hadoop clusters consist of a NameNode that manages the cluster’s namespace and multiple DataNodes that store and retrieve the data. HDFS stores data as blocks rather than complete files. Data replication creates copies of each block as part of HDFS’s fault-tolerant design.
Yet Another Resource Negotiator (YARN): Resource Management and Job Scheduling
YARN schedules and manages a Hadoop cluster’s resources. A YARN Resource Manager coordinates the YARN NodeManagers that monitor the MapReduce task execution in each DataNode.
Hadoop MapReduce
Hadoop MapReduce is a Java-based programming model for creating distributed data processing applications. MapReduce jobs maximize reliability by writing interim results to hardware for retrieval at the next step in the processing workflow. This model requires extensive storage infrastructure and is read/write intensive, adding latency to all workloads.
Although based on Java, the MapReduce programming language is not widely understood. Experienced engineers are hard to recruit and retain. In addition, few data consumers can write MapReduce programs, adding more work to overburdened data teams.
Key components and tools in the Hadoop ecosystem
The Hadoop ecosystem comprises dozens of open-source projects under the Apache Hadoop umbrella or as Apache top-level projects. Some popular components of the Hadoop ecosystem include:
Data ingestion
Apache Flume
Flume is a distributed service for aggregating streaming data. Its fault-tolerant, high-availability design can reliably ingest real-time data streams into Hadoop’s centralized repositories.
Apache Sqoop
Data teams used Sqoop to transfer large batches of data between relational databases and Hadoop. Although promoted to top-level status in 2012, a lack of developer interest led Apache to retire the project in 2021.
Data storage
HBase
HBase non-relational databases give organizations real-time access to huge data tables stored in HDFS. Years before Google made its Bigtable system a commercial service, HBase brought Bigtable features to the Hadoop platform.
Apache Cassandra
Cassandra is a NoSQL database for high-availability applications. With no single points of failure or network bottlenecks, Cassandra provides a performant, fault-tolerant option for cloud data storage.
Learn more about the Starburst Cassandra Connector.
Apache Hive
Apache Hive is an enhancement to Hadoop that translates queries using HQL, Hive’s version of SQL, into MapReduce commands. Although more accessible than MapReduce, Hive’s extra steps add even more latency to Hadoop processing jobs.
Learn more about the Starburst Hive Connector.
Data processing
Apache Mahout
Apache Mahout is a framework for developing the linear algebra algorithms common to machine learning and data science applications.
Apache Pig
Apache Pig is a tool for developing parallelized data processing applications. The framework comprises two elements: the high-level Pig Latin programming language and an engine that compiles Pig Latin into MapReduce programs.
Apache Spark
Apache Spark is an SQL-based data processing engine optimized for machine learning and data science applications.
Apache Storm
Apache Storm processes real-time data streams to support use cases like continuous computation and real-time analytics.
Data management
Apache ZooKeeper
Apache ZooKeeper stores hierarchical key-value pairs to configure and synchronize cloud applications.
Apache Ambari
Apache Ambari streamlines Hadoop cluster administration in large organizations by centralizing provisioning, management, and monitoring.
Workflow management
Apache Oozie
Oozie integrates with Hadoop to manage MapReduce and other jobs.
Data serialization
Apache Avro
Avro is a row-based file format for storing record data in Hadoop and other platforms. Every Avro file includes JSON schema descriptions to make the data self-describing and accessible to any program reading it.
Apache Parquet
Apache Parquet is a column-based file format with efficient compression algorithms to reduce storage costs and rich metadata to optimize query performance.
Security components
Apache Ranger
Apache Ranger gives Hadoop enterprise-ready data security features, including a centralized UI, granular authorization rules, and support for various access control models. These features extend to other Hadoop-compatible frameworks, like Apache Kafka and HBase.
Apache Knox
Apache Knox’s reverse proxy is a gateway between security services and Hadoop APIs. Knox can protect multiple Hadoop clusters while giving a company’s security stack a single gateway for interacting with the clusters’ APIs.
Challenges with the Hadoop Ecosystem
Although Hadoop and its ecosystem have evolved to address new business requirements, the ecosystem faces several challenges in the modern era of petabyte-scale data processing.
Scalability: Hadoop’s origins in the data center make the platform difficult and expensive to scale as the volume, velocity, and variety of enterprise data expand.
Performance and Costs: Many Hadoop ecosystem projects exist to address the limitations of HDFS and MapReduce. Hadoop’s native write-intensive processing demands ever-increasing levels of storage infrastructure to provide just-in-case capacity for large jobs.
Security and governance: Hadoop’s developers assumed the platform would run behind a data center’s secure perimeter, so they did not build security features into the original framework. More robust governance capabilities are available, but securing Hadoop remains complex.
Complexity: Hadoop’s core modules lack features critical to enterprise computing today. As a result, companies must assemble a complex patchwork of open-source and commercial technologies that are increasingly difficult to administer.
Trino’s capabilities
Developers at Facebook created Trino (formerly PrestoSQL) to address the Hadoop ecosystem’s weaknesses in ad hoc and iterative analytics. Today, Trino supports many big data use cases, including ETL batch processing and near real-time analytics. Trino’s capabilities include:
Decoupling storage and compute
Trino-based analytics lets companies break the chains holding storage and compute together. They can use their choice of cloud object storage service to store structured, semi-structured, and unstructured data while independently scaling compute to meet demand.
Federation creates a single point of access
Trino connectors for over fifty enterprise data sources unify a company’s data architecture within a single interface. Rather than promising a mythical single source of truth, Trino establishes a single point of access that lets authorized users access data across the organization.
Performance and cost optimization
Filters, pushdown queries, and other Trino features maximize query performance while minimizing compute consumption and network congestion.
ANSI-standard SQL accessibility
Trino uses ANSI-standard SQL, making big data analytics accessible to users at all experience levels. Data scientists can use the query language directly in their Python algorithms, while engineers can build Trino-based pipelines that query multiple sources simultaneously. At the other end of the scale, analysts can use Tableau, Excel, or other familiar software to support decision-makers.
Migrating from Hadoop to Trino
Migrating from the Hadoop ecosystem to a modern open data lakehouse gives enterprises a future-proof, scalable, performant, cost-effective architecture for fostering data-driven decision-making. This migration requires establishing the three elements of an open data lakehouse.
Move to object storage
Cloud object storage provides the foundation for building an open data lakehouse. Services like Amazon S3 and Azure Blob Storage can store any type of data efficiently and reliably. Data engineering teams can quickly scale storage capacity to handle the ever-increasing data flows from OLTP and streaming data sources.
Modernize table formats
An object storage service’s flat structure and limited metadata cannot support advanced data analysis. Iceberg is the ideal open table format for building a data lakehouse. The data is saved in Parquet, ORC, or Avro file formats. Iceberg maintains table state within a series of metadata files. Any change gets recorded into new metadata files that atomically replace old ones.
One advantage of this approach is that Iceberg manages schema evolution within its metadata files without requiring table data rewrites. Likewise, snapshots of Iceberg tables enable features like time travel and rollbacks.
Upgrade the query engine
Hadoop-based analytics requires complex, brittle, and expensive ETL data pipelines to integrate the disparate enterprise data sources needed for big data analytics.
An open data lakehouse uses Trino’s federation capabilities to integrate a company’s data architecture into a single analytics platform. Users can explore and discover data residing in a relational database or a clickstream as easily as data in the object store. Standard SQL statements let business intelligence analysts conduct ad hoc analyses, data scientists perform iterative experiments, and business users access dashboards and reports.
Starburst’s enterprise-ready Trino implementation further enhances the query engine with governance, data management, and performance optimizations:
- Granular access controls let administrators enforce security and governance policies at the row and column levels.
- Data engineers use Starburst’s automatic data cataloging to streamline adding new data sources.
- Starburst’s autonomous indexing and smart caching accelerate query performance up to seven-fold.
Combining Starburst and Iceberg with commodity object storage into an open data lakehouse unifies data architectures into a single source of access for all users, democratizing access and enabling data-driven business cultures.
