I'm currently working on a new project that will have both Python and Octave script working on data. I'm evaluating some data format, one of these is parquet. There is no problem Python side and I know that Matlab have some support for this kind of data (yeah I know Matlab and Octave are not the same).
Does anyone know if octave can read or write parquet files? I wasn't able to find anything about this online.
Thank you
Related
I am learning Analysing data for my research. There is a website that contains all the data set for every day for the past 26 years. I have to write a python code such that if I enter the date, the data set for that day should open. Since the files are in .cdf format I have to use python to open them. Can someone tell me what are the things that I need to learn and what are the libraries that will help me to open the data set from the website without first downloading them? I have some experience with python but not a lot.
Also is there any good source that I can visit to learn more about Data Analysis using python?
You can use Pandas for this matter.
Pandas lets you store files on a dataframe using a link, without downloading it to your local machine.
I am currently trying to search a group of ebooks to learn more about C#. The aim is to ask a question get a page in one or multiple of the ebooks to read. I went to the g_suite chat team and they have kindly directed me to vision commands that was easy enough to follow to make multiple json files.
https://cloud.google.com/vision/docs/pdf
I want to implement this files in to AUTO ML Natural Language Processing. To do so, a CSV file is required.
I do not know how to create a CSV file that would get me past this point and I am currently stuck.
How to create a CSV file using gcloud command and should not the Json file be Jsonl file to be accepted?
thanks for your answer in advance
The output from the Vision API (service) is a JSON file written to Cloud Storage.
The input dataset to Auto ML expects the data to be in CSV format and stored in Cloud Storage.
This isn't a gcloud issue but a general data-transformation problem: transforming JSON to CSV.
Google Cloud includes services that could help you with this but I suggest you start by writing a script that converts the data (i.e. loads then parses the JSON file creating a CSV file in the required format for Auto ML).
You may want to Google to see whether others have done similar and use their code as a starting point.
NOTE IIUC your solution, while an interesting use of these technologies may be overkill. If you're looking to learn Vision API and Auto ML, great. If not, most of this content is available more directly as searchable HTML and text on the web and indeed Stack overflow exists to answer developer questions on a myriad of topics including C#.
Technologies I am using to fetch data from my MySQL database is Spark 2.4.4 and Scala. I want to display that data in my Angular8 project. Any help on how to do it? I could not find any documentation regarding this.
I am not sure if this is a scala/spark related question. It sounds more towards system design of your project.
One solution is to use your Angular8 directly read from MySQL. There are tons of tutorials online.
Another solution is to use your spark/scala to read data and dump to CSV/JSON file at somewhere and use Angular8 to read in that file. The pros is that you can do some transformation before displaying your data. The cons is that there is latency between transformation and displaying. After reading the flat file into JSON it's up to you how to render that data on user's screen.
Pardon my simple question but I'm relatively new to Spark/Hadoop.
I'm trying to load a bunch of small CSV files into Apache Spark. They're currently stored in S3, but I can download them locally if that simplifies things. My goal is to do this as efficiently as possible. It seems like it would be a shame to have some single-threaded master downloading and parsing a bunch of CSV files while my dozens of Spark workers sit idly. I'm hoping there's an idiomatic way to distribute this work.
The CSV files are arranged in a directory structure that looks like:
2014/01-01/fileabcd.csv
2014/01-01/filedefg.csv
...
I have two years of data, with directories for each day, and a few hundred CSVs inside of each. All of those CSVs should have an identical schema, but it's of course possible that one CSV is awry and I'd hate for the whole job to crash if there are a couple problematic files. Those files can be skipped as long as I'm notified in a log somewhere that that happened.
It seems that every Spark project I have in mind is in this same form and I don't know how to solve it. (e.g. trying to read in a bunch of tab-delimited weather data, or reading in a bunch of log files to look at those.)
What I've Tried
I've tried both SparkR and the Scala libraries. I don't really care which language I need to use; I'm more interested in the correct idioms/tools to use.
Pure Scala
My original thought was to enumerate and parallelize the list of all year/mm-dd combinations so that I could have my Spark workers all processing each day independently (download and parse all CSV files, then stack them on top of eachother (unionAll()) to reduce them). Unfortunately, downloading and parsing the CSV files using the spark-csv library can only be done in the "parent"/master job, and not from each child as Spark doesn't allow job nesting. So that won't work as long as I want to use the Spark libraries to do the importing/parsing.
Mixed-Language
You can, of course, use the language's native CSV parsing to read in each file then "upload" them to Spark. In R, this is a combination of some package to get the file out of S3 followed by a read.csv, and finishing off with a createDataFrame() to get the data into Spark. Unfortunately, this is really slow and also seems backwards to the way I want Spark to work. If all my data is piping through R before it can get into Spark, why bother with Spark?
Hive/Sqoop/Phoenix/Pig/Flume/Flume Ng/s3distcp
I've started looking into these tailored tools and quickly got overwhelmed. My understanding is that many/all of these tools could be used to get my CSV files from S3 into HDFS.
Of course it would be faster to read my CSV files in from HDFS than S3, so that solves some portion of the problem. But I still have tens of thousands of CSVs that I need to parse and am unaware of a distributed way to do that in Spark.
So right now (Spark 1.4) SparkR has support for json or parquet file structures. Csv files can be parsed, but then the spark context needs to be started with an extra jar (which needs to be downloaded and placed in the appropriate folder, never done this myself but my collegues have).
sc <- sparkR.init(sparkPackages="com.databricks:spark-csv_2.11:1.0.3")
sqlContext <- sparkRSQL.init(sc)
There is more information in the docs. I expect that a newer spark release would have more support for this.
If you don't do this you'll need to either resort to a different file structure or use python to convert all your files from .csv into .parquet. Here is a snippet from a recent python talk that does this.
data = sc.textFile(s3_paths, 1200).cache()
def caster(x):
return Row(colname1 = x[0], colname2 = x[1])
df_rdd = data\
.map(lambda x: x.split(','))\
.map(caster)
ddf = sqlContext.inferSchema(df_rdd).cache()
ddf.write.save('s3n://<bucket>/<filename>.parquet')
Also, how big is your dataset? You may not even need spark for analysis. Note that also as of right now;
SparkR has only DataFrame support.
no distributed machine learning yet.
for visualisation you will need to convert a distributed dataframe back into a normal one if you want to use libraries like ggplot2.
if your dataset is no larger than a few gigabytes, then the extra bother of learning spark might not be worthwhile yet
it's modest now, but you can expect more from the future
I've run into this problem before (but w/ reading a large qty of Parquet files) and my recommendation would be to avoid dataframes and to use RDDs.
The general idiom used was:
Read in a list of the files w/ each file being a line (In the driver). The expected output here is a list of strings
Parallelize the list of strings and map over them with a customer csv reader. with the return being a list of case classes.
You can also use flatMap if at the end of the day you want a data structure like List[weather_data] that could be rewritten to parquet or a database.
I have downloaded "High Resolution Initial Conditions" climate forecast data for one day, it was in extension .tar.gz so I extracted it in my local directory and I get the files like in the attached image. I think, that the files without extension are GRIB data (because first word in them is "GRIB"). So I want to get data from the big files (GRIB and NetCDF formats containing climate data like temerature & pressure in grid) to my database, but they are binary. Can you recommend me some easy way for getting data from these files? I can't get any information about handling their datasets on their website.
Converting these files to .csv would be nice, but I can't find a program to convert the GRIB files.
Using python and some available modules it is simple...
The Enthought Python Distribution includes several packages, including netCDF4, to deal with NetCDF files!
I've never worked with GRIB files, but google tells that another python package exists, pygrib2.
Or you can use PyNio, a Python package that allows to read and write netCDF3 and netCDF4 classic format, and to read GRIB1 and GRIB2 files.
I don't know the ammount of data you have, but usually it is crazy to convert it to *.csv! Python is easy to learn, and suitable to work with this kind of data (with matplotlib package you can even plot it). Or, if you really need it in a *.csv, you can select with python a smaller domain, for example, or the needed variables...
For conversion into text, look into http://www.cpc.ncep.noaa.gov/products/wesley/wgrib.html or http://www.cpc.ncep.noaa.gov/products/wesley/wgrib2/
Both are C programs from one of the big names in GRIB.
I'm currently dealing with a similar issue.
In my case I'm trying to rely on the GrADS software, which can "easily" transform GRIB data into other formats.
If your dataset is not huge, then you can export it to csv using this tutorial.
My dataset is 80gb in GRIB binary files, so I'm very restricted in what software I can use to handle it (no R unless I find a computer with more than 80gb of RAM).