Analytics
37 items tagged with "analytics"
Best Practices1
Tutorials2
How to convert data to Apache Parquet for analytics
Convert CSV or JSON to columnar Parquet with partitioning and compression to speed up and shrink analytic queries.
How to model an analytics table in ClickHouse
Create a MergeTree table in ClickHouse with the right ordering key and partitioning, then load data and run fast aggregations.
Products8
R
Programming language for statistical computing and graphics
Elasticsearch
Distributed search and analytics engine
InfluxDB
Time series database for metrics and events
ClickHouse
Column-oriented database for real-time analytics
TimescaleDB
Time-series database built on PostgreSQL
Firebase
Google's app development platform
Grafana
Analytics and monitoring platform
New Relic
Observability platform for full-stack visibility
Stacks4
Modern Data Stack
Snowflake/BigQuery, dbt, Airflow, Looker - Analytics pipeline
Modern Data Stack (ELT)
Cloud-native ELT pattern: managed ingestion loads raw data into a warehouse, where dbt transforms it and a BI tool serves analytics.
dbt + BigQuery Analytics Stack
Serverless analytics engineering on Google BigQuery with dbt managing SQL transformations, tests, and lineage on top of warehouse-native compute.
ClickHouse Real-Time Analytics Stack
High-performance analytics stack: ClickHouse ingests event streams from Kafka and serves sub-second OLAP queries powering user-facing dashboards.
Comparisons7
Snowflake vs BigQuery
Cloud-agnostic data warehouse with decoupled compute versus Google's fully serverless analytics warehouse.
Snowflake vs Redshift
Cloud-agnostic warehouse with auto-scaling compute versus Amazon Redshift's AWS-native MPP data warehouse.
BigQuery vs Redshift
Google's fully serverless analytics warehouse versus Amazon Redshift's AWS-native MPP warehouse.
ClickHouse vs Druid
Columnar OLAP database for fast analytics versus Apache Druid's real-time analytics datastore for time-series and event data.
dbt vs Dataform
The widely adopted SQL transformation framework versus Google's warehouse-native transformation tool integrated with BigQuery.
Data Lake vs Data Warehouse
A data lake stores raw data of any type cheaply; a data warehouse stores structured, modeled data for fast analytics. They serve different stages and users.
ETL vs ELT
ETL transforms data before loading it; ELT loads raw data first and transforms inside the destination. Cloud warehouses have made ELT increasingly common.
Benchmarks7
TPC-H
An ad-hoc decision-support benchmark of 22 complex analytical queries over a star-like schema, reporting query throughput and power at fixed scale factors.
TPC-DS
A modern decision-support benchmark with 99 queries over a multi-snowflake retail schema, designed to stress complex analytics, data loading, and concurrency.
ClickBench
An open benchmark for analytical databases using a single wide web-analytics table and 43 queries to compare cold and hot OLAP query latency.
Star Schema Benchmark (SSB)
A simplified TPC-H derivative using a classic star schema and 13 queries in four flights to measure data-warehouse query performance.
TPCx-BB (BigBench)
An end-to-end big-data analytics benchmark mixing SQL, machine learning, and natural-language processing over a retail data set on Hadoop/Spark-style platforms.
dbt Pipeline Performance Benchmark
A category of benchmarks measuring transformation pipeline performance in dbt — model build time, warehouse compute cost, and incremental run efficiency.
LDBC Social Network Benchmark
The standard benchmark for graph databases, measuring interactive transactional and analytical query performance over a realistic, correlated social-network graph.
FAQs4
What is the difference between OLTP and OLAP?
OLTP (Online Transaction Processing) handles many short, concurrent transactions like orders, payments, and updates, optimized for fast writes and row...
What is the difference between a data warehouse, a data lake, and a lakehouse?
A data warehouse stores structured, modeled data optimized for fast SQL analytics, typically loaded through schema-on-write pipelines; examples includ...
What is a materialized view?
A materialized view is a database object that stores the precomputed result of a query physically on disk, unlike a regular view which runs its query ...
What is columnar storage and why is it faster for analytics?
Columnar storage organizes data on disk by column rather than by row, so all values of a single column are stored together. Analytical queries that sc...
Glossaries4
OLAP
OLAP (Online Analytical Processing) refers to systems optimized for complex analytical queries over large volumes of historical data, enabling aggregation, slicing, and multidimensional analysis for reporting and decision-making.
Data Warehouse
A data warehouse is a centralized analytical database that stores integrated, structured data from multiple sources, optimized for querying and reporting rather than transactional processing.
Data Lake
A data lake is a centralized repository that stores large volumes of raw data in its native format — structured, semi-structured, and unstructured — at low cost, with schema applied at read time rather than on ingestion.
Data Lakehouse
A data lakehouse is an architecture that combines the low-cost, flexible storage of a data lake with the management, transactions, and performance of a data warehouse, using open table formats over object storage.