geoparquet
delta
geoparquet | delta | |
---|---|---|
3 | 69 | |
729 | 6,958 | |
4.9% | 2.2% | |
5.5 | 9.8 | |
6 days ago | 1 day ago | |
Python | Scala | |
Apache License 2.0 | Apache License 2.0 |
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geoparquet
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Friends don't let friends export to CSV
That's why I'm working on the GeoParquet spec [0]! It gives you both compression-by-default and super fast reads and writes! So it's usually as small as gzipped CSV, if not smaller, while being faster to read and write than GeoPackage.
Try using `GeoDataFrame.to_parquet` and `GeoPandas.read_parquet`
[0]: https://github.com/opengeospatial/geoparquet
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COMTiles (Cloud Optimized Map Tiles) hosted on Amazon S3 and Visualized with MapLibre GL JS
GeoParquet
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Postgres and Parquet in the Data Lke
> "Generating Parquet"
It is also useful for moving data from Postgres to BigQuery! ( batch load )
https://cloud.google.com/bigquery/docs/loading-data-cloud-st...
Thanks for the "ogr2ogr" trick! :-)
I hope the next blog post will be about GeoParquet and storing complex geometries in parquet format :-)
https://github.com/opengeospatial/geoparquet
delta
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Delta Lake vs. Parquet: A Comparison
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
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Understanding Parquet, Iceberg and Data Lakehouses
I often hear references to Apache Iceberg and Delta Lake as if they’re two peas in the Open Table Formats pod. Yet…
Here’s the Apache Iceberg table format specification:
https://iceberg.apache.org/spec/
As they like to say in patent law, anyone “skilled in the art” of database systems could use this to build and query Iceberg tables without too much difficulty.
This is nominally the Delta Lake equivalent:
https://github.com/delta-io/delta/blob/master/PROTOCOL.md
I defy anyone to even scope out what level of effort would be required to fully implement the current spec, let alone what would be involved in keeping up to date as this beast evolves.
Frankly, the Delta Lake spec reads like a reverse engineering of whatever implementation tradeoffs Databricks is making as they race to build out a lakehouse for every Fortune 1000 company burned by Hadoop (which is to say, most of them).
My point is that I’ve yet to be convinced that buying into Delta Lake is actually buying into an open ecosystem. Would appreciate any reassurance on this front!
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Getting Started with Flink SQL, Apache Iceberg and DynamoDB Catalog
Apache Iceberg is one of the three types of lakehouse, the other two are Apache Hudi and Delta Lake.
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[D] Is there other better data format for LLM to generate structured data?
The Apache Spark / Databricks community prefers Apache parquet or Linux Fundation's delta.io over json.
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Delta vs Iceberg: make love not war
Delta 3.0 extends an olive branch. https://github.com/delta-io/delta/releases/tag/v3.0.0rc1
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Databricks Strikes $1.3B Deal for Generative AI Startup MosaicML
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 https://delta.io . They integrate pretty seamlessly to other data visualisation tooling if you want to use it for that but their built in graphs are fine for most cases. They also have ML on rails type through menus and models if I recall but I typically don't use it for that. I've typically used it for ETL or ELT type workflows for data that's too big or isn't stored in a database.
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The "Big Three's" Data Storage Offerings
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).
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Ideas/Suggestions around setting up a data pipeline from scratch
As the data source, what I have is a gRPC stream. I get data in protobuf encoded format from it. This is a fixed part in the overall system, there is no other way to extract the data. We plan to ingest this data in delta lake, but before we do that there are a few problems.
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Medallion/lakehouse architecture data modelling
Take a look at Delta Lake https://delta.io, it enables a lot of database-like actions on files
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CSV or Parquet File Format
I prefer parquet (or delta for larger datasets. CSV for very small datasets, or the ones that will be later used/edited in Excel or Googke sheets.
What are some alternatives?
mbtiles-spec - specification documents for the MBTiles tileset format
dvc - 🦉 ML Experiments and Data Management with Git
odbc2parquet - A command line tool to query an ODBC data source and write the result into a parquet file.
Apache Cassandra - Mirror of Apache Cassandra
geemap - A Python package for interactive geospatial analysis and visualization with Google Earth Engine.
lakeFS - lakeFS - Data version control for your data lake | Git for data
flatgeobuf - A performant binary encoding for geographic data based on flatbuffers
hudi - Upserts, Deletes And Incremental Processing on Big Data.
postgres_vectorization_test - Vectorized executor to speed up PostgreSQL
delta-rs - A native Rust library for Delta Lake, with bindings into Python
BlenderGIS - Blender addons to make the bridge between Blender and geographic data
iceberg - Apache Iceberg