Delta Lake
Amazon SageMaker
GeoSpock
Cloud Dataprep
Apache Zeppelin
Palantir Foundry
Databricks Unified Analytics Platform
Azure Synapse Analytics
Amazon EMR
Google BigQuery
Google Cloud Dataflow
Google Cloud Dataproc
Databricks
Qubole
Snowflake
HortonWorks Data Platform
Delta Lake Amazon EMRBased on our record, Delta Lake should be more popular than Amazon EMR. It has been mentiond 31 times since March 2021. 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.
Delta is pretty great, let's you do upserts into tables in DataBricks much easier than without it. I think the website is here: https://delta.io. - Source: Hacker News / 4 months ago
Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake. - Source: dev.to / 5 months ago
The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json. Source: 5 months ago
Databricks provides Jupyter lab like notebooks for analysis and ETL pipelines using spark through pyspark, sparkql or scala. I think R is supported as well but it doesn't interop as well with their newer features as well as python and SQL do. It interfaces with cloud storage backend like S3 and offers some improvements to the parquet format of data querying that allows for updating, ordering and merged through... - Source: Hacker News / 10 months ago
Structured, Semi-structured and Unstructured can be stored in one single format, a lakehouse storage format like Delta, Iceberg or Hudi (assuming those don't require low-latency SLAs like subsecond). Source: 11 months ago
There are different ways to implement parallel dataflows, such as using parallel data processing frameworks like Apache Hadoop, Apache Spark, and Apache Flink, or using cloud-based services like Amazon EMR and Google Cloud Dataflow. It is also possible to use parallel dataflow frameworks to handle big data and distributed computing, like Apache Nifi and Apache Kafka. Source: about 1 year ago
I'm going to guess you want something like EMR. Which can take large data sets segment it across multiple executors and coalesce the data back into a final dataset. Source: almost 2 years ago
This is exactly the kind of workload EMR was made for, you can even run it serverless nowadays. Athena might be a viable option as well. Source: almost 2 years ago
Apache Spark is one of the most actively developed open-source projects in big data. The following code examples require that you have Spark set up and can execute Python code using the PySpark library. The examples also require that you have your data in Amazon S3 (Simple Storage Service). All this is set up on AWS EMR (Elastic MapReduce). - Source: dev.to / over 2 years ago
Check out https://aws.amazon.com/emr/. Source: about 2 years ago
Amazon SageMaker - Amazon SageMaker provides every developer and data scientist with the ability to build, train, and deploy machine learning models quickly.
Google BigQuery - A fully managed data warehouse for large-scale data analytics.
GeoSpock - GeoSpock is the platform for data lake management, providing a unified view of the data assets within an organization and making it easily accessible.
Google Cloud Dataflow - Google Cloud Dataflow is a fully-managed cloud service and programming model for batch and streaming big data processing.
Cloud Dataprep - Cloud Dataprep by Trifacta is a data prep & cleansing service for exploring, cleaning & preparing datasets using a simple drag & drop browser environment
Google Cloud Dataproc - Managed Apache Spark and Apache Hadoop service which is fast, easy to use, and low cost