Apache Pig
Looker
Jupyter
Presto DB
Google BigQuery
Databricks
Rakam
Informatica
Apache Spark
Apache Flink
Hadoop
Apache Kafka
Apache Storm
Apache Hive
Redis
Spring Batch
Apache Pig Apache SparkBased on our record, Apache Spark seems to be a lot more popular than Apache Pig. While we know about 70 links to Apache Spark, we've tracked only 2 mentions of Apache Pig. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Pig, a platform/programming language for authoring parallelizable jobs. - Source: dev.to / over 2 years ago
In the early days of the Big Data era when K8s hasn't even been born yet, the common open source go-to solution was the Hadoop stack. We have written several old-fashioned Map-Reduce jobs, scripts using Pig until we came across Spark. Since then Spark has became one of the most popular data processing engines. It is very easy to start using Lighter on YARN deployments. Just run a docker with proper configuration... - Source: dev.to / over 3 years ago
Apache Iceberg defines a table format that separates how data is stored from how data is queried. Any engine that implements the Iceberg integration — Spark, Flink, Trino, DuckDB, Snowflake, RisingWave — can read and/or write Iceberg data directly. - Source: dev.to / 22 days ago
Apache Spark powers large-scale data analytics and machine learning, but as workloads grow exponentially, traditional static resource allocation leads to 30–50% resource waste due to idle Executors and suboptimal instance selection. - Source: dev.to / 24 days ago
One of the key attributes of Apache License 2.0 is its flexible nature. Permitting use in both proprietary and open source environments, it has become the go-to choice for innovative projects ranging from the Apache HTTP Server to large-scale initiatives like Apache Spark and Hadoop. This flexibility is not solely legal; it is also philosophical. The license is designed to encourage transparency and maintain a... - Source: dev.to / 2 months ago
[1] S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach. Pearson, 2020. [2] F. Chollet, Deep Learning with Python. Manning Publications, 2018. [3] C. C. Aggarwal, Data Mining: The Textbook. Springer, 2015. [4] J. Dean and S. Ghemawat, "MapReduce: Simplified Data Processing on Large Clusters," Communications of the ACM, vol. 51, no. 1, pp. 107-113, 2008. [5] Apache Software Foundation, "Apache... - Source: dev.to / 2 months ago
If you're designing an event-based pipeline, you can use a data streaming tool like Kafka to process data as it's collected by the pipeline. For a setup that already has data stored, you can use tools like Apache Spark to batch process and clean it before moving ahead with the pipeline. - Source: dev.to / 3 months ago
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
Apache Flink - Flink is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations.
Jupyter - Project Jupyter exists to develop open-source software, open-standards, and services for interactive computing across dozens of programming languages. Ready to get started? Try it in your browser Install the Notebook.
Hadoop - Open-source software for reliable, scalable, distributed computing
Presto DB - Distributed SQL Query Engine for Big Data (by Facebook)
Apache Kafka - Apache Kafka is an open-source message broker project developed by the Apache Software Foundation written in Scala.
