I have uploaded files in azure data lake by python in gen1. Those files, which exist in azure data lake, I need to apply elastic search to those files (the files can be .pdf, .csv, .xlsx, .doc.) using python django.
This article helps you to index and query large amounts of structured data by combining ADLS and Elasticsearch using third-party tool called Dremio.
About Dremio: Dremio provides a self-service semantic layer and governance for your data. Dremio’s semantic layer is an integrated, searchable catalog in the Data Graph that indexes all of your metadata, allowing business users to easily make sense of the data in the data lake. Anything created by users—spaces, directories, and virtual datasets make up the semantic layer, all of which is indexed and searchable. The relationships between your data sources, virtual datasets, and all your queries are also maintained in the Data Graph, creating a data lineage, allowing you to govern and maintain your data.
Azure Data Lake Store is a highly scalable and secure data storage and analytics service that deals with big data problems easily. It provides a variety of functions and solutions for data management and governance.
Elasticsearch is a powerful search and analytics engine. It is highly popular due to the scale-out architecture, JSON data model, and text search capabilities. Also, with the help of Elasticsearch, you can index and query large amounts of structured data, use convenient RESTful API, and more.
Hope this helps.
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I am looking for guidance/best practice to approach a task. I want to use Azure-Databricks and PySpark.
Task: Load and prepare data so that it can be efficiently/quickly analyzed in the future. The analysis will involve summary statistics, exploratory data analysis and maybe simple ML (regression). Analysis part is not clearly defined yet, so my solution needs flexibility in this area.
Data: session level data (12TB) stored in 100 000 single line JSON files. JSON schema is nested, includes arrays. JSON schema is not uniform but new fields are added over time - data is a time-series.
Overall, the task is to build an infrastructure so the data can be processed efficiently in the future. There will be no new data coming in.
My initial plan was to:
Load data into blob storage
Process data using PySpark
flatten by reading into data frame
save as parquet (alternatives?)
Store in a DB so the data can be quickly queried and analyzed
I am not sure which Azure solution (DB) would work here
Can I skip this step when data is stored in efficient format (e.g. parquet)?
Analyze the data using PySpark by querying it from DB (or from blob storage when in parquet)
Does this sound reasonable? Does anyone has materials/tutorials that follow similar process so I could use them as blueprints for my pipeline?
Yes, it's sound reasonable, and in fact it's quite standard architecture (often referred as lakehouse). Usual implementation approach is following:
JSON data loaded into blob storage are consumed using Databricks Auto Loader that provides efficient way of ingesting only new data (since previous run). You can trigger pipeline regularly, for example, nightly, or run it continuously if data arriving all the time. Auto Loader is also handling schema evolution of input data.
Processed data is better to store as Delta Lake tables that provide better performance than "plain" Parquet due use of additional information in the transaction log so it's possible to efficiently access only necessary data. (Delta Lake is built on top of Parquet, but has more capabilities).
Processed data then could be accessed via Spark code, or via Databricks SQL (it could be more efficient for reporting, etc., as it's heavily optimized for BI workloads). Due the big amount of data, storing them in some "traditional" database may not be very efficient or be very costly.
P.S. I would recommend to look on implementing this with Delta Live Tables that may simplify development of your pipelines.
Also, you may have access to Databricks Academy that has introductory courses about lakehouse architecture and data engineering patterns. If you don't have access to it, you can at least look to Databricks courses published on GitHub.
Can a MariaDB be used with Zarr or migrated to Zarr in a lossless fashion, if so please provide some guidance on how this can be achieved?
I have searched the Zarr docs and MariaDB docs and did not find enough information on this topic. I don't want to lose or modify any of the data and I would like to be able to decompress or restore the data to it's original MariaDB state. I receive output in the form of a 4TB MariaDB (10.2) containing multiple tables of various dimensions and multiple variable types. I am using python (+3.6) and would like to take advantage of Zarr for the purpose of being able to perform exploratory data analysis on the data contained across multiple tables in the MariaDB while it is compressed in an effort to save local disk space. The storage of the data and processing of the data is all done locally and there is no plan to utilize cloud services.
I have considered converting the MariaDB to a sqlite database with Python but stopped looking into that route as I understand this could lead to a loss/corruption of data.
Thank you advance,
Brian
I have collected a large Twitter dataset (>150GB) that is stored in some text files. Currently I retrieve and manipulate the data using custom Python scripts, but I am wondering whether it would make sense to use a database technology to store and query this dataset, especially given its size. If anybody has experience handling twitter datasets of this size, please share your experiences, especially if you have any suggestions as to what database technology to use and how long the import might take. Thank you
I recommend using a database schema for this, especially considering it's size. (this is without knowing anything about what the dataset holds) That being said, I suggest now or for future questions of this nature using the software suggestions website for this plus adding more about what the dataset would look like.
As for suggesting a certain database in specific, I recommend doing some research about what each do but for something that just holds data with no relations any will do and could show great query improvement vs just txt files as query's can be cached and data is faster to retrieve due to how databases store and lookup files weather it just be hashed values or whatever they use.
Some popular databases:
MYSQL, PostgreSQL - Relational Databases (simple and fast and easy to use/setup but need some knowledge of SQL)
MongoDB - NoSQL Database (also easy to use and setup and no SQL needed, it relies more on dicts to access DB through the API. Also memory mapped so can be faster than Relational but need to have enough RAM for the Indexes.)
ZODB - Full Python NoSQL Database (Kind of like MongoDB but written in Python)
These are very light and brief explanations of each DB, be sure to do your research before using them, they each have their pros and cons. Also, remember this is just a couple of many popular and highly used Databases, there's also TinyDB, SQLite (comes with Python), and PickleDB that are full Python but are generally for small applications.
My experience is mainly with PostgreSQL, TinyDB, and MongoDB, my favorite being MongoDB and PGSQL. For you, I'd look at either of those but don't limit yourself there's a slue of them plus many drivers that help you write easier/less code if that's what you want. Remember google is your friend! And welcome to Stack Overflow!
Edit
If your dataset is and will remain fairly simple but just large and you want to keep with using txt files, consider pandas and maybe a JSON or a csv format and library. It can greatly help and increase efficiency when querying/managing data like this from txt files plus less memory usage as it won't always or ever need the entire dataset in memory.
you can try using any NOSql DB. Mongo DB would be a good place to start
In this illuminating answer a technique is presented to get the data sizes from statically coded datasets that live in a Google Big Query Project.
To further automate this, I am operating in a Python Jupyter notebook environment using the BQ wrapper to make BQ Queries inside the Python. I would like to construct a Big Query that will fetch all the datasets that live in my Project (they have the form of an 8- or 9-digit identifier) as a first step to feed into a Python data structure, then use the referenced answer programmatically to get table sizes for all the identified datasets. Is there a way to do this?
Setting up a data warehousing mining project on a Linux cloud server. The primary language is Python .
Would like to use this pattern for querying on data and storing data:
SQL Database - SQL database is used to query on data. However, the SQL database stores only fields that need to be searched on, it does NOT store the "blob" of data itself. Instead it stores a key that references that full "blob" of data in the a key-value Blobstore.
Blobstore - A key-value Blobstore is used to store actual "documents" or "blobs" of data.
The issue that we are having is that we would like more frequently accessed blobs of data to be automatically stored in RAM. We were planning to use Redis for this. However, we would like a solution that automatically tries to get the data out of RAM first, if it can't find it there, then it goes to the blobstore.
Is there a good library or ready-made solution for this that we can use without rolling our own? Also, any comments and criticisms about the proposed architecture would also be appreciated.
Thanks so much!
Rather than using Redis or Memcached for caching, plus a "blobstore" package to store things on disk, I would suggest to have a look at Couchbase Server which does exactly what you want (i.e. serving hot blobs from memory, but still storing them to disk).
In the company I work for, we commonly use the pattern you described (i.e. indexing in a relational database, plus blob storage) for our archiving servers (terabytes of data). It works well when the I/O done to write the blobs are kept sequential. The blobs are never rewritten, but simply appended at the end of a file (it is fine for an archiving application).
The same approach has been also used by others. For instance:
Bitcask (used in Riak): http://downloads.basho.com/papers/bitcask-intro.pdf
Eblob (used in Elliptics project): http://doc.ioremap.net/eblob:eblob
Any SQL database will work for the first part. The Blobstore could also be obtained, essentially, "off the shelf" by using cbfs. This is a new project, built on top of couchbase 2.0, but it seems to be in pretty active development.
CouchBase already tries to serve results out of RAM cache before checking disk, and is fully distributed to support large data sets.
CBFS puts a filesystem on top of that, and already there is a FUSE module written for it.
Since fileststems are effectively the lowest-common-denominator, it should be really easy for you to access it from python, and would reduce the amount of custom code you need to write.
Blog post:
http://dustin.github.com/2012/09/27/cbfs.html
Project Repository:
https://github.com/couchbaselabs/cbfs