I would like to ask if anyone could tell me or refer me to an internet page which describes all possibilities to store data in an apache hadoop cluster.
What I would like to know is: Which type of data should be stored in which "system". Under type of data I mean for example:
Live data (realtime)
Historical data
Data which is regularly accessed from an application
...
The complete question is not reduced on Hbase or Hive ("System") but for everything which is available under Hdp.
I hope someone could lead me in a direction where i could find my answer. Thanks!
I can give you an overview, but rest of the things you have to read on your own.
Let's begin with the types of data you want to store in HDFS:
Data in Motion(Which you denoted as real-time data).
So, how can you fetch the real-time data? Is it even possible? The answer is NO. There will always be a delay. However, we can reduce the downtime and processing time of the data. For which we have HDF(Hortonworks Data Flow). It works with the data in motion. There are many services providing the real-time data streaming. You can take the example of Kafka, Nifi, Storm and many more. These tools are used to process the data. You also need to store the data in such a way that you'd be able to fetch it no time(~2 sec), for that we use HBase. HBase stores the data in the columnar structure.
Data at rest (Historic/Data stored for future use)
So, to store the data at rest, there are no such issues. HDP(Hortonworks Data Platform) is there providing us the services to ingest, store and process the data. Even we can integrate HDF services to HDP(prior to version 2.6), which makes it easier to process Data in motion also. Here we need Databases to store a large amount of data. However, we are provided with HDFS(Hadoop Distributed File System) which can help us store any kind of data. But we don't ONLY want to store our data, we want to fetch it no time when it is required. So, how are we planning to do that? By storing our data in a structured form. For which we are provided Hive and HBase. To store such amount of data which is in TB, we need to run heavy processes that are where MapReduce, YARN, Spark, Kubernetes, Spark comes in to picture.
This is the basic idea of storing and processing data in Hadoop.
Rest you can always read from the internet.
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I'm in the process of analyzing Azure Stream Analytics to replace a stream processing solutions based on NiFi with some REST microservices.
One step is the enrichment of sensor data form a very large database of sensors (>120Gb).
Is it possible with Azure Stream Analytics? I tried with a very small subset of the data (60Mb) and couldn't even get it to run.
Job logs give me warnings of memory usage being too high. Tried scaling to 36 stream units to see if it was even possible, to no avail.
What strategies do I have to make it work?
If I deterministically (via a hash function) partition the input stream using N partitions by ID and then partition the database using the same hash function (to get id on stream and ID on database to the same partition) can I make this work? Do I need to create several separated stream analytics jobs do be able to do that?
I suppose I can use 5Gb chunks, but I could not get it to work with ADSL Gen2 datalake. Does it really only works with Azure SQL?
Stream Analytics supports reference datasets of up to 5GB. Please note that large reference datasets come with the downside of making jobs/nodes restarts very slow (up to 20 minutes for the ref data to be distributed; restarts that may be user initiated, for service updates, or various errors).
If you can downsize that 120Gb to 5Gb (scoping only the columns and rows you need, converting to types that are smaller in size), then you should be able to run that workload. Sadly we don't support partitioned reference data yet. This means that as of now, if you have to use ASA, and can't reduce those 120Gb, then you will have to deploy 1 distinct job for each subset of stream/reference data.
Now I'm surprised you couldn't get a 60Mb ref data to run, if you have details on what exactly went wrong, I'm happy to provide guidance.
I am looking for the best option to access data from Spark data pipelines. The scenario is as follows:
I am reading data from Kafka topics, creating a streaming dataframe which is then cleaned and being printed on the console. I need this data to be integrated with existing Python scripts which is doing all the data operations by Pandas. I have considered the following options:
Write streaming data to local memory (e.g. Hive Tables).
Use Spark Structured Streaming ForeachBatch Sink.
I want to mention that the data is to be read after a certain interval and there will be a real time data dashboard in the future with this data.
Please advise which will be the best approach to handle this scenario. Apologies if the question sounds too basic. Thanks in advance.
If you save data on Hive each time before accessing the newly streamed data through python scripts, the newly added hive partitions are required to be refreshed each time as well.
https://cwiki.apache.org/confluence/display/Hive/LanguageManual+DDL#LanguageManualDDL-RecoverPartitions(MSCKREPAIRTABLE)
Here are some disadvantages of having a hive for mentioned real-time scenarios.
https://www.quora.com/What-are-some-disadvantages-of-Apache-Hive#
Whereas, Using Spark Structured Streaming looks a better choice for the near-real-time experience.
https://databricks.com/blog/2017/04/04/real-time-end-to-end-integration-with-apache-kafka-in-apache-sparks-structured-streaming.html
I am trying to build a ETL process that extracts data out to GemFire and load in Teradata. However, I am not finding a good mechanism to export data out. The only thing I have found so far is rest api that gets all entries from a region. However, is this good for bulk export ? It will give data back in json which has to be parsed before loading in table, which I assume won't be very performant for large volume of data. Is there any other solution to this ? Like exporting data as csv from GemFire? Or ODBC/JDBC connection to GemFire ? I found both the bulk export and ODBC/JDBC in Gemfire XD documentation but not in core GemFire? So are they not supported in core GemFire? What is the difference between core GemFire and the XD version ?
These are two different products designed with different things in mind. GemFire XD provides a low-latency SQL interface to in-memory table data, so it's generally used as an in-memory RDBMS. GemFire, on the other hand, is an in-memory data grid with "no restrictions" regarding the data you insert into the regions, you basically deal with custom java objects, not with tables. Also, I know GemFire XD was built on top of GemFire in the past, not sure what's the current status of that (you might want to have a look at Snappy Data for more details).
That said, and strictly speaking of GemFire, you can export snapshots of your regions and import them afterwards into another cluster. Even better, you can read a snapshot entry by entry for further processing or transformation into other formats, which I believe is exactly what you're looking for. Please have a look at Cache and Region Snapshots to get the details.
Hope this helps. Cheers.
I am creating an ETL pipeline that uses variety of sources and sends the data to Big Query. Talend cannot handle both relational and non relational database components in one job for my use case so here's how i am doing it currently:
JOB 1 --Get data from a source(SQL Server, API etc), transform it and store transformed data in a delimited file(text or csv)
JOB 1 -- Use the stored transformed data from delimited file in JOB 1 as source and then transform it according to big query and send it.
I am using delimited text file/csv as intermediary data storage to achieve this.Since confidentiality of data is important and solution also needs to be scalable to handle millions of rows, what should i use as this intermediary source. Will a relational database help? or delimited files are good enough? or anything else i can use?
PS- I am deleting these files as soon as the job finishes but worried about security till the time job runs, although will run on safe cloud architecture.
Please share your views on this.
In Data Warehousing architecture, it's usually a good practice to have the staging layer to be persistent. This gives you among other things, the ability to trace the data lineage back to source, enable to reload your final model from the staging point when business rules change as well as give a full picture about the transformation steps the data went through from all the way from landing to reporting.
I'd also consider changing your design and have the staging layer persistent under its own dataset in BigQuery rather than just deleting the files after processing.
Since this is just a operational layer for ETL/ELT and not end-user reports, you will be paying only for storage for the most part.
Now, going back to your question and considering your current design, you could create a bucket in Google Cloud Storage and keep your transformation files there. It offers all the security and encryption you need and you have full control over permissions. Big Query works seemingly with Cloud Storage and you can even load a table from a Storage file straight from the Cloud Console.
All things considered, whatever the direction you chose I recommend to store the files you're using to load the table rather than deleting them. Sooner or later there will be questions/failures in your final report and you'll likely need to trace back to the source for investigation.
In a nutshell. The process would be.
|---Extract and Transform---|----Load----|
Source ---> Cloud Storage --> BigQuery
I would do ELT instead of ETL: load the source data as-is and transform in Bigquery using SQL functions.
This allows potentially to reshape data (convert to arrays), filter out columns/rows and perform transform in one single SQL.
Users of our platform will have large amounts of stored data on our system. Through an application, once connected, that data will be transferred to them and no longer need to remain on our servers. There could potentially be hundreds or thousands of users connected at any given time, performing their downloads.
Here's the proposed architecture:
User management, configuration, and data download statistics will be maintained in a SQL Server database, while using either Redis or DynamoDB for the large data sets.
The reason for choosing either Redis or DynamoDB is based on cost - cheaper than running another SQL Server instance, and performance. The data format will be similar to a datamart - flat table with no joins.
Initially the queries would be simple - get all data for user X between a date range, and optionally delete.
Since we may want to add free text searching for certain fields of that data using elasticsearch may be a better option to use from the get-go.
I want this to be auto-scaling but not sure which database would be best to use for this scenario.
Here's some great discussion on Database + Search tier from AWS ReInvent:
https://youtu.be/K7o5OlRLtvU?t=1574
I would not take Elastic-search alone because it does not provide auto-scaling for writing capacity. In fact, it's not trivial to augment the number of shard of an index. Secondly it can only handle the JSON format, which could be an issue for you.
Redis could be a good idea because it is really fast, everything is done in RAM, and it provides keys with a limited time-to-live which could be interesting for you. Unfortunately, if your data size exceeds the capacity in RAM of your amazon instance you will have to shard your Redis database. And Redis does not support it, you will have to deal it on your application code. Moreover, as far as I know Redis does not handle complex queries. You will also need to save your data in a Redis data structure which could be an issue for you
DynamoDB handles auto-scaling really well but on the other hand it is a key/value database so it does not allow you to make queries like "get all data for user X between a date range". DynamoDB also allows you to save your data in any format.
The solution will be to use either DynamoDB or either Redis depending of the size of your datas, and to use ElasticSearch in order to index your key with only the meta-data (user and dates). Like that your index will be small, and if you lost the ability to index because of ElasticSearch get too buzy, you keep the ability to save user's datas.