I am trying to use the university's cluster, I have the link of the uploaded dataset.
The question is: do I have to transfer the data to my storage or not?
if it's not, how can I access and use the dataset?
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We are currently running spark jobs, store the data in HDFS and create Hive tables on top of it. Now, we are planning to migrate from HDFS to GFS to store the Hadoop data. Is it possible to use GFS as our Hadoop File System in our case, either by mounting or CIFS share or using GFS directly?
The following link talks about remote mounting of devices, but it mentions that it is not specific to any of the details.
https://www.cloudera.com/documentation/other/reference-architecture/topics/ra_storage_device_accepta...
Please refer me to any document that explains in detail about remote mounting of devices.
I am trying to build a "Data Lake" from scratch. I understand how a data lake works and the purpose of it; it's all over the internet. But when the question arises how to build one from scratch there is no source. I want to understand if:
Data warehouse + Hadoop = Data Lake
I know how to run Hadoop and bring in data into Hadoop.
I want to build a sample on premise data lake to demo my manager. Any help is appreciated.
You'd have to have structured and unstructured data to make a Hadoop cluster into a data lake.
So, you'd have to have some ETL pipeline taking the unstructured data and converting it to structured data. Product reviews or something similar would provide your unstructured data. Converting this to something usable by Hive (as an example) would give you your structured data.
I would look at https://opendata.stackexchange.com/ for getting your data and google Hadoop ETL for ideas on how to cleanse the data. It's up to you how you want to write your pipeline (Spark or MapReduce).
You can build datalake using AWS services. A simple way to do so is to use an AWS CloudFormation template to configure the solution, including AWS services such as Amazon S3 for unlimited data storage, Amazon Cognito for authentication, Amazon Elasticsearch for strong searching capabilities, AWS Lambda for microservices, AWS Glue for data transmission, and Amazon Athena for data analytics. The following figure represents the complete architecture of building a data lake on AWS using AWS services.
Refer this article for reference: https://medium.com/#pmahmoudzadeh/building-a-data-lake-on-aws-3f02f66a079e
I'm working on Windows command line as problems with Unix and firewalls prevent gsutil from working. I can read my Google Cloud Storage files and copy them over to other buckets (which I don't need to do). What I'm wondering is how to download them directly into HDFS (which I'm 'ssh'ing into)? Has anyone done this? Ideally this is part one, part two is creating Hive tables for the Google Cloud Storage data so we can use HiveQL and Pig.
You can use the Google Cloud Storage connector which provides an HDFS-API compatible interface to your data already in Google Cloud Storage, so you don't even need to copy it anywhere, just read from and write directly to your Google Cloud Storage buckets/objects.
Once you set up the connector, you can also copy data between HDFS and Google Cloud Storage with the hdfs tool, if necessary.
I'm working with EMR (Elastic MapReduce) on AWS infrastructure and the default way to provide input files (large datasets) for programs is to upload them to an S3 bucket and reference those buckets from within EMR.
Usually I download the datasets to my local,development machine and then upload them to S3, but this is getting harder to do with larger files, as upload speeds are generally much lower than download speeds.
My question is is there a way to download files from the internet (given their URL) directly into S3 so I don't have to download them to my local machine and then manually upload them?
No. You need an intermediary- typically, an EC2 instance is used, rather than your local machine, for speed.
I want to transfer Un-semi structured data(MS word/PDF/JSON) from a remote computer into hadoop(could be in batch and could be near realtime but not stream).
I have to Make sure that data is moved quickly from Remote location to my local machine(working on low bandwidth)into HDFS or local machine.
Fro example Internet Download Manager has this amazing technique of making several connections with the FTP and utilizing low bandwidth with more connections.
Is there any possibility that Hadoop ecosystem provides such a tool to ingest data into hadoop. Or any self made technique?
Which Tool/Technique could be better.
You could use the Web HDFS API http://hadoop.apache.org/docs/current/hadoop-project-dist/hadoop-hdfs/WebHDFS.html#Document_Conventions