We are working with Azure cloud and we have some pipelines which ingest daily data from sap to azure data lake gen 2. We were working with Azure Data Factory ingesting json and csv files but maybe is better change our approach and ingest parquet files to landing zone and then move to trusted zone using delta tables.
What do you think? is a good idea ingesting raw data to landing in parquet format?
Related
I was following the tutorial about data transformation with azure databricks, and it says before loading data into azure synapse analytics, the data transformed by azure databricks would be saved on temp storage in azure blob storage first before loading into azure synapse analytics. Why the need to save it to a temp storage before loading into azure synapse analytics?
The Azure storage container acts as an intermediary to store bulk data when reading from or writing to Azure Synapse. Spark connects to the storage container using one of the built-in connectors: Azure Blob storage or Azure Data Lake Storage (ADLS) Gen2.
The following architecture diagram shows how this is achieved with each HDFS bridge of the Data Movement Service (DMS) service on every Compute node connecting to an external resource such as Azure Blob Storage. PolyBase then bidirectionally transfers data between SQL Data Warehouse and the external resource providing the fast load performance.
Using PolyBase to extract, load and transform data
The steps for implementing a PolyBase ELT for SQL Data Warehouse are:
Extract the source data into text files.
Load the data into Azure Blob storage, Hadoop, or Azure Data Lake Store.
Import the data into
SQL Data Warehouse staging tables using PolyBase.
Transform the data(optional).
Insert the data into production tables.
I'm very new to Druid and want to know how we can ingest Parquet files on S3 into Druid?
We get data in CSV format and we standardise it to Parquet format in the Data Lake. This then needs to be loaded into Druid.
Instead of trying to ingest parquet files from S3, I streamed data to a Kinesis topic and used that as a source for Druid.
You have to add druid-parquet-extensions in the druid.extensions.loadList in the common.runtime.properties file.
After that you can restart the Druid server.
However, only ingesting a parquet file from local source is documented. I couldn't verify loading from S3 as my files were encrypted.
Try adding the above extension and then read from S3 just like you'd ingest a regular file from S3.
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
We have created a new data lake in Hadoop file system. Data is stored in the form of ORC. Currently analytical system is directly connecting to data lake to read these ORC file.
Is there any way to create a middle layer between data lake and analytical system to serve data ?
Which are your analytical software?
Is it possible to use the API or the RESTful web services to access the data lake?
Please, clarify a little more.
The question is very generic, but one common and easy way to build a data lake is to use Presto (https://prestodb.io).
Presto can read several formats, but also connect to different data sources like mysql databases and others, presenting the data as table.
Clients can use SQL, also through jdbc/odbc and hence access the data lake even from tools like excel, or other analytical tools (microstrategy, tableau, etc).
I've setted up Syncfusion Dashboard Platform to work with some imported test data (Syncfusion Big Data).
I have used Integration platform for import in Hadoop (files are stored in Avro format).
Then I put Avro data from Hadoop to Hive tables via Spark SQL (convert Avro to table), in total I have 400 rows.
Then I used this Hive2 as Datasource in Dashboard platform.
Then I tried to fetch the data and it works very slowly.
Can somebody point me how to figure it out? Settings in Syncfusion are by default.
Thanks!
Recommended Solution:
As Hive Server2(Map Reduce) is well suited for batch processing with large data set, I recommend you to use Spark SQL data source for near real time analytics such as dashboard visualization. Because Spark SQL process data in-memory to avoid multiple disk I/O operations.
• Tables created under the Hive can also be accessed from “Spark SQL” in Syncfusion distribution as both uses same meta store database.
So you can use the “Spark SQL” connection type in Syncfusion Dashboard platform instead of “Hive”.