Summary
Data processing tools are software applications that collect, clean, transform, and analyze raw data to make it usable for various purposes, such as business intelligence, data analysis, and machine learning. These tools are essential for handling large volumes of data and extracting meaningful insights.
What they do:
- Collect and ingest data
They gather data from various sources, including databases, files, and APIs.
- Clean and transform data
They handle tasks like data cleansing (removing errors, duplicates, and inconsistencies), data transformation (converting data into a usable format), and data validation.
- Analyze data
They perform various analytical operations, such as filtering, aggregating, and summarizing data, to derive insights.
- Output data:
They can output processed data in various formats for further use, such as in reports, dashboards, or machine learning models.
Source: Gemini AI Overview
OnAir Post: Processing & Analytics Tools
About
Apache Spark
Overview:
Apache Spark™ is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Key Features:
Batch/streaming data
Unify the processing of your data in batches and real-time streaming, using your preferred language: Python, SQL, Scala, Java or R.
SQL analytics
Execute fast, distributed ANSI SQL queries for dashboarding and ad-hoc reporting. Runs faster than most data warehouses.
Data science at scale
Perform Exploratory Data Analysis (EDA) on petabyte-scale data without having to resort to downsampling
Machine learning
Train machine learning algorithms on a laptop and use the same code to scale to fault-tolerant clusters of thousands of machines.
Source: Apache Spark Website
Apache Hadoop
The Apache® Hadoop® project develops open-source software for reliable, scalable, distributed computing.
The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.
Source: Apache Hadoop Website
Apache Kafka
Overview:
Apache Kafka is an open-source distributed event streaming platform used by thousands of companies for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications.
Core Capabilities
- High Throughput
Deliver messages at network limited throughput using a cluster of machines with latencies as low as 2ms. - Scalable
Scale production clusters up to a thousand brokers, trillions of messages per day, petabytes of data, hundreds of thousands of partitions. Elastically expand and contract storage and processing. - Permanent storage
Store streams of data safely in a distributed, durable, fault-tolerant cluster. - High availability
Stretch clusters efficiently over availability zones or connect separate clusters across geographic regions.
Ecosystem
- Built-in Stream Processing
Process streams of events with joins, aggregations, filters, transformations, and more, using event-time and exactly-once processing. - Connect To Almost Anything
Kafka’s out-of-the-box Connect interface integrates with hundreds of event sources and event sinks including Postgres, JMS, Elasticsearch, AWS S3, and more. - Client Libraries
Read, write, and process streams of events in a vast array of programming languages. - Large Ecosystem Open Source Tools
Large ecosystem of open source tools: Leverage a vast array of community-driven tooling.
Trust & Ease of Use
- Mission Critical
Support mission-critical use cases with guaranteed ordering, zero message loss, and efficient exactly-once processing. - Trusted By Thousands of Orgs
Thousands of organizations use Kafka, from internet giants to car manufacturers to stock exchanges. More than 5 million unique lifetime downloads. - Vast User Community
Kafka is one of the five most active projects of the Apache Software Foundation, with hundreds of meetups around the world. - Rich Online Resources
Rich documentation, online training, guided tutorials, videos, sample projects, Stack Overflow, etc.
Source: Apache Hadoop Website
Data Analytics Tools
Summary
Source: Google Gemini Overview
Data engineering and data analytics tools are crucial for managing, processing, and analyzing data to derive insights. Data engineering tools focus on building the infrastructure for data pipelines, while data analytics tools are used for visualizing and interpreting data. Key tools include Apache Spark, Apache Airflow, Snowflake, Databricks, and tools for ETL, SQL, Python, and cloud-based data warehousing.
Data engineers and analysts utilize these tools to ensure data quality, accessibility, and usability for informed decision-making.
Data Analytics Tools
Source: Google Gemini Overview
Tableau, Power BI, Looker
Popular business intelligence and data visualization tools for creating interactive dashboards and reports.
dbt
An open-source command-line tool and framework for transforming data in the warehouse, often used in analytics engineering workflows.
Metabase
An open-source tool for creating and sharing dashboards and reports, designed to be user-friendly for non-technical users.
ThoughtSpot
A visualization tool that integrates with the cloud ecosystem and offers interactive dashboards and visuals.
Apache Hive
A data warehousing system built on top of Hadoop for querying and analyzing large datasets using SQL-like syntax.
Redash
An open-source platform for connecting to and querying various data sources, creating dashboards, and sharing visualizations.
Data Visualization Libraries (e.g., Matplotlib, Seaborn)
Python libraries used to create various types of charts and graphs for data exploration and presentation.
Big Data Processing Frameworks (e.g., Hadoop)
These tools enable the processing of massive datasets, often distributed across multiple machines.
