· Analytics & Data Warehousing Databases
Analytics & Data Warehousing Databases

Analytics & Data Warehousing Databases

Analytics and data warehousing platforms enable large-scale data processing, business intelligence, and analytical workloads that power data-driven decision making. Cloud-native warehouses dominate the modern landscape, with Snowflake appearing in >10% of Data Engineering positions, representing the shift toward scalable, managed analytics infrastructure. BigQuery serves Google Cloud ecosystems while Redshift integrates with AWS, each appearing in >5% of data engineering roles. Databricks bridges data warehousing and machine learning workloads, present in >10% of Data Engineering and MLOps positions. Traditional enterprise solutions like Teradata and SAP HANA maintain presence in established organizations but show declining market share. The entry-level market strongly favors cloud platforms, particularly Snowflake (>10% in entry-level Data Engineering roles), as organizations migrate from on-premise to cloud-based analytics. These platforms are central to data engineering careers, with expertise determining capability to build scalable data pipelines, perform complex analytics, and enable organizational data strategies.

Cloud-Native Data Warehouses

Modern cloud-based analytics platforms offering elasticity, separation of storage and compute, and managed operations. These platforms dominate data engineering and analytics roles, with Snowflake leading adoption, BigQuery serving GCP environments, and Redshift integrating with AWS. Strong entry-level opportunities exist, particularly for Snowflake and BigQuery.

Snowflake

High Demand
Rank: #1
Entry-Level: Moderate
Leading cloud data warehouse in Data Engineering (>10%), Data Analytics (>5%), and analytics-focused roles. Strong entry-level demand with >10% prevalence in entry-level Data Engineering positions. Cloud-agnostic platform. Used for centralized data warehousing, ELT pipelines, business intelligence analytics, data sharing across organizations, semi-structured data processing (JSON, Avro), and scalable analytics without infrastructure management.

BigQuery

Moderate Demand
Rank: #2
Entry-Level: Low
Google Cloud's serverless data warehouse in Data Engineering (>5%), E-commerce Backend Development (>5%), and GCP-centric environments. Lower entry-level presence with >5% prevalence. Fully managed and serverless. Used for petabyte-scale analytics, real-time analytics on streaming data, machine learning with BigQuery ML, log analytics, SQL queries on massive datasets, and GCP data lake architectures.

Redshift

Moderate Demand
Rank: #3
Entry-Level: Low
AWS cloud data warehouse in Data Engineering (>5%), Data Analytics (>5%), and AWS-integrated analytics stacks. Lower entry-level demand with >5% prevalence. Tightly integrated with AWS services. Used for AWS data lake analytics, business intelligence on historical data, ETL/ELT pipelines with AWS Glue, columnar storage analytics, and organizations standardized on AWS infrastructure.

Databricks

Moderate Demand
Rank: #4
Entry-Level: Low
Unified analytics platform in Data Engineering (>10%), MLOps (>5%), Machine Learning Engineering (>5%), and data science workflows. Lower entry-level accessibility with >5% prevalence. Built on Apache Spark. Used for lakehouse architectures combining data lakes and warehouses, collaborative data science notebooks, large-scale ETL with Spark, machine learning pipelines, streaming analytics, and Delta Lake for ACID transactions on data lakes.

Enterprise Data Warehouses

Traditional on-premise and hybrid data warehousing platforms with deep enterprise roots. Teradata and SAP HANA serve established large organizations with legacy analytics infrastructure. These platforms show declining but persistent demand with minimal entry-level opportunities, primarily relevant for maintaining existing enterprise systems.

Teradata

Low Demand
Rank: #1
Entry-Level: Low
Legacy enterprise data warehouse with limited presence in modern job market (<5% prevalence). Very rare entry-level opportunities. Historically dominant in large enterprises. Used for maintaining legacy enterprise analytics systems, high-performance parallel processing, complex query optimization in established installations, large retail and financial institutions, and organizations with significant Teradata investments.

SAP HANA

Low Demand
Rank: #2
Entry-Level: Low
SAP's in-memory database platform in Enterprise Systems Development (>5%), Database Administration (<5%), and SAP-centric organizations. Limited entry-level accessibility. Used for real-time analytics on transactional data, SAP S/4HANA applications, in-memory computing for fast queries, SAP Business Warehouse (BW) on HANA, combining OLTP and OLAP workloads, and organizations heavily invested in SAP ecosystem.