This document outlines the big data landscape in 2016, including key components like data lakes, data warehouses, ingestion, processing, data science, analytics, and data sources. It also discusses related microservices, algorithms, data storage technologies, data workflows, stream processing systems, SQL and NoSQL databases, and specialized databases for time series, graphs, and other data types. The goal is to provide an overview of the different technologies and approaches for working with large and diverse datasets.

1. Big Data Landscape 2016
User Interface
Data Lake
Data Warehouse
Ingestion
Processing
Data Science Interactive Analysis Reporting & Dashboards
Data Sources
Analytics
Micro Analytics Services: Substitute for
reporting servers.
Charting Libraries:Dashboards:
Analytics Frontends
Algorithms
Structured Data Lake: The eternal memory.
Efficient data serialization formats:
 Integated compression
 Column-oriented storage
 Predicate pushdown
Distributed Filesystem or NoSQL DB
Data Workflows ETL Jobs Massive
Parallelization
Pig Open Studio
Data Logicstics Stream
Processing
NewSQL: SQL meets NoSQL.
Polyglott Persistence & Analytics
Index Machines: Fast aggregation and search.
In-Memory Databases: Fast access.
Time Series Databases
Atlas
Graph Databases