Is it possible to configure dynamoDB instead of Cassandra. If possible can please share any instructions on it. I couldn't find any in https://github.com/uber/cadence/blob/master/docker/README.md
It is not possible as DynanamoDB is not fully compatible with Cassandra semantic as related to conditional batch operations.
Related
I'm trying to explore Memgraph a bit since I love Cypher. The website mentions querying with Cypher, but I'm wondering whether creating/modifying with Cypher is also possible.
The website also mentions a bunch of other cool features, which I may end up also using (Kafka streaming straight into the store seems very cool)
It pretty much boils down to
Is Memgraph a persistent store?
Can I also just use Cypher to create/modify nodes?
The website seems to put great emphasis on the whole real-time aspect, can you explain a bit about what that means?
Yes, Memgraph is a persistent store for graph data. It uses two mechanisms to ensure the durability of the stored data: write-ahead logging (WAL) and
taking periodic snapshots. You can read more about storage in our docs at
https://memgraph.com/docs/memgraph/concepts/storage .
Memgraph uses the Cypher query language, so you can use it like you’ve used it in Neo4j, with a couple of differences mentioned here:
https://memgraph.com/docs/cypher-manual/differences
In our documentation, you can also find the whole Cypher manual with examples of how to use it with Memgraph.
When it comes to streaming, Memgraph along with Kafka allows you to stream your data and analyze it in real-time.
I'm looking for a graph exploration tool similar to https://github.com/prabushitha/gremlin-visualizer for querying AWS Neptune while using openCypher to enjoy the new offering:
https://aws.amazon.com/blogs/database/announcing-opencypher-for-amazon-neptune-building-better-graph-applications-with-opencypher-and-gremlin-together/.
I'm familiar with the Jupyter notebook https://github.com/aws/graph-notebook but I'm looking for other alternatives.
With the recent release of openCypher on Neptune we have provided support for querying and visualizing results of openCypher queries via the Jupyter notebook as you have mentioned. This tool is good for writing and visualizing queries but does not have graph exploration functionality for clicking on and expanding connected nodes/edges.
However with the release of openCypher Neptune supports interoperability between Gremlin and openCypher on top of the same data. This means that you can load the data one time and use either query language. This allows you to use any of the graph exploration tooling that works with Gremlin, such as https://github.com/prabushitha/gremlin-visualizer or https://www.tomsawyer.com/graph-database-browser to provide graph exploration capabilities without having to reload the data.
I've been developing Spark jobs for some years using on-premise clusters and our team recently moved to the Google Cloud Platform allowing us to leverage the power of BigQuery and such.
The thing is, I now often find myself writing processing steps in SQL more than in PySpark since it is :
easier to reason about (less verbose)
easier to maintain (SQL vs scala/python code)
you can run it easily on the GUI if needed
fast without having to really reason about partitioning, caching and so on...
In the end, I only use Spark when I've got something to do that I can't express using SQL.
To be clear, my workflow is often like :
preprocessing (previously in Spark, now in SQL)
feature engineering (previously in Spark, now mainly in SQL)
machine learning model and predictions (Spark ML)
Am I missing something ?
Is there any con in using BigQuery this way instead of Spark ?
Thanks
A con I can see is the additional time required by the Hadoop cluster to create and finish the job. By making a direct request to BigQuery, this extra time can be decreased.
If your tasks need parallel processing, I would recommend using Spark, but if your app is mainly used to access to BQ, you might want to use the BQ Client Libraries and separate your current tasks:
BigQuery Client Libraries. They are optimized to connect to BQ. Here is a QuickStart and you can use different programming languages like python or java, among others.
Spark jobs. If you still need to perform transformations in Spark and need to read the data from BQ you can use the Dataproc-BQ connector. While this connector is installed in Dataproc by default, you can install it on-premises so that you can continue running you SparkML jobs with BQ data. Just in case it helps, you might want to consider using some GCP services like AutoML, BQ ML, AI Platform Notebooks, etc., they are specialized services for Machine Learning and AI.
I'm using PySpark (on GCP Dataproc), BigQuery and we have jobs in both. I will summarize my vision about Pros and Cons of one system against the other. And I do admit that your environment could be different, so that something which I think is Pros might not be like this for you.
Pros of Spark:
better testing of the code, simpler to build unit tests and run them with mocked data and classes, rather in trying to do this with BigQuery
it's possible to use SQL (SparkSQL) for operations and even combine operations over different data sources (DB, files, BQ)
we have JSON files in the format which is not valid for BigQuery, and it cannot parse them (while files have valid JSON format)
possible to implement naturally more complicated logic for some cases, for example, traversing arrays in nested fields and other complicated calculations
better custom monitoring is possible, when we need to check specific metrics in the pipeline we can send related metrics (StatsD, etc.) easier
more natural for CI/CD processes
Pros of BigQuery (all with a note: if all data is available):
simplicity of SQL, when all data is available in a convenient format
DBAs who are not familiar with Python/Scala still could contribute (bcs they know SQL)
awesome infrastructure behind the scene, very performant
With both approaches it's possible to check quickly the result in GUI. For example, Jupyter Notebook allows to run PySpark instantly. I cannot add my notes about ML related traits, though.
Following this answer I wonder what are the principal differences (if any) between WriteToBigQuery and BigQuerySink of the Apache Beam Python SDK.
What are the considerations or limitations of using one over another?
Looking at sources:
BigQuerySink triggers a Dataflow native sink for BigQuery
that only supports batch pipelines. Instead of using this sink
directly, please use WriteToBigQuery transform that works for both
batch and streaming pipelines.
They both seem to do a similar thing underneath otherwise.
I am new to Hadoop and trying to learn it on datawarehousing and analytical front.
Can someone advise me on how to set up my practice machines, especially with regards to
1.Number of machines/nodes required to start learning
2.Is it advisable to set up on Windows?
3.What software needs to be installed
4.Availability of test/sample data
Also I would like to get advice on the best way to perform BI actions with Hive.
Thank you.
I would suggest to download cloudera VM if you more interested in hadoop machinery. Another way to jump start immidiately - to use amazon EMR (elastic mapreduce). There is an option to create interactive hive cluster there and start playing with datasets stored in S3.
Regarding number of nodes - it depends on goals. If you interested to "feel" some hadoop performance - try at least 4-6 nodes.
Both ways listed above are good if you do not have access to organization's internal hadoop / hive cluster. And even in this case - I would suggest to try with them to gain some hands-on before using shared environment.