The shortage of data scientists – those triple-threat types who possess advanced statistics, business, and coding skills – has been well-documented over the years. But increasingly, businesses are facing a shortage of another key individual on the big data team who’s critical to achieving success – the data engineer.
Data engineers are experts in designing, building, and maintaining the data-based systems in support of an organization’s analytical and transactional operations. While they don’t boast the quantitative skills that a data scientist would use to, say, build a complex machine learning model, data engineers do much of the other work required to support that data science workload, such as:
Building data pipelines to collect data and move it into storage;
Preparing the data as part of an ETL or ELT process;
Stitching the data together with scripting languages;
Working with the DBA to construct data stores;
Ensuring the data is ready for use;
Using frameworks and microservices to serve data.
Read the whole thing. My experience is that most shops looking to hire a data scientist really need to get data engineers first; otherwise, you’re wasting that high-priced data scientist’s time. The plus side is that if you’re already a database developer, getting into data engineering is much easier than mastering statistics or neural networks.
While this seems like a lot of parts just to copy a few files, it’s important to note I only scratched the surface of what ADF can do. Think of ADF as an airline company that manages and enables cargo (data) movement between cities (data sources). A pipeline represents the overall goal of moving certain cargo from one city to another. The linked service is the airport, which provides a landing point and access control for the cities. The dataset is the list of cargo to move, the activity is the flight itself, while the integration runtime is the airport infrastructure that makes the cargo movement possible. A single pipeline requires all these objects to run successfully; however, many pipelines can use these same objects to complete different tasks. Once you’ve created these data factory objects, it is straightforward to layer on additional functionality or more pipelines. ADF also has visual tools that make building these objects a breeze – to build my pipeline, all I had to do was click on “Copy data” in the visual tools start menu and follow the steps provided.
Matt has a video demonstrating the process as well.
This is a quick post to share a few scripts to find what is currently executing in Azure Data Factory. These PowerShell scripts are applicable to ADF version 1 (not version 2 which uses different cmdlets).
Prerequisite: In addition to having installed the Azure Resource Manager modules, you’ll have to register the provider for Azure Data Factory:#One-time registration of the ADF provider #Register-AzureRmResourceProvider -ProviderNamespace Microsoft.DataFactory
Click through for the Powershell snippets.
The data within the Data Lake store was organised into a Year and Month hierarchy for the folders, and each days transactions were stored in a file which was named after the day within the relevant month folder. The task then was to create a pipeline which copies the dataset in the Data Lake Store over to the dbo.Orders table in Azure SQL DB every day within the scheduled period (Q1 2016).
After creating all the json scripts and deploying them (with no errors), I clicked on the ‘Monitor and Manage’ tile to monitor the activities, check everything was working as it should be and monitor the progress. After waiting for at least 10 minutes, I started to get frustrated.
Click through for the fix and an explanation.
For Hadoop developer, the actual game starts after the data is being loaded in HDFS. They play around this data in order to gain various insights hidden in the data stored in HDFS.
So, for this analysis the data residing in the relational database management systems need to be transferred to HDFS. The task of writing MapReduce code for importing and exporting data from relational database to HDFS is uninteresting & tedious. This is where Apache Sqoop comes to rescue and removes their pain. It automates the process of importing & exporting the data.
Sqoop makes the life of developers easy by providing CLI for importing and exporting data. They just have to provide basic information like database authentication, source, destination, operations etc. It takes care of remaining part.
Sqoop internally converts the command into MapReduce tasks, which are then executed over HDFS. It uses YARN framework to import and export the data, which provides fault tolerance on top of parallelism.
In my experience, Sqoop does two things really well: first, it lets you move data from a relational database into HDFS (or Hive). Second, it lets you move data from HDFS (or Hive) into a staging table on a relational database. That can make Sqoop a useful part of an ETL process.
The general steps are
Set up your source query.
Pass the data through a Lookup for your Dimension with the missing results routed to a “No Match” output.
Insert those “No Match” rows into your Dimension using a SQL task – checking to make sure that this particular row hasn’t already been inserted (this is important).
Do another lookup using a “Partial Cache” to catch these newly-inserted members.
Use a UNION ALL transform to bring the existing and late-arriving members together.
Click through for more information and a helpful package diagram.
When your pipeline already has to read every line of your data, it’s tempting to make it perform some fancy transformations. But you should steer clear of these add-ons so that you:
Avoid flawed calculations. If you have thousands of machines running your pipeline in real-time, sure, it’s easy to collect your data — but not so easy to tell if those machines are performing the right calculations.
Won’t limit yourself to the aggregates you decided on in the past. If you’re performing actions on your data as it streams by, you only get one shot. If you change your mind about what you want to calculate, you can only get those new stats going forward — your old data is already set in stone.
Won’t break the pipeline. If you start doing fancy stuff on the pipeline, you’re eventually going to break it. So you may have a great idea for a new calculation, but if you implement it, you’re putting the hundreds of other calculations used by your coworkers in jeopardy. When a pipeline breaks down, you may never get that data.
The problem is that even if the cost of storage is much cheaper than before, there’s a fairly long tail before you get into potential revenue generation. I like the idea, but selling it is hard when you generate a huge amount of data.
With the launch of AWS Glue, AWS provides a portfolio of services to architect a Big Data platform without managing any servers or clusters. AWS Glue is a fully managed extract, transform, and load (ETL) service that makes it easy for customers to prepare and load their data for analytics. You can create and run an ETL job with a few clicks in the AWS Management Console. You simply point AWS Glue to your data stored on AWS, and AWS Glue discovers your data and stores the associated metadata (for example, the table definition and schema) in the AWS Glue Data Catalog. After it’s cataloged, your data is immediately searchable, queryable, and available for ETL.
AWS Glue generates the code to execute your data transformations and data loading processes. Furthermore, AWS Glue provides a managed Spark execution environment to run ETL jobs against a data lake in Amazon S3. In short, you can now run a Lambda Architecture in AWS in a completely 100% serverless fashion!
“Serverless” applications allow you to build and run applications without thinking about servers. What this means is that you can now stream data in real-time, process huge volumes of data in S3, and run SQL queries and visualizations against that data without managing server provisioning, installation, patching, or capacity scaling. This frees up your users to spend more time interpreting the data and deriving business value for your organization.
Laith has a working demo of the process available as well.
The solution picks up the SQL data changes from the CDC Change Tracking system tables, creates JSON messages from the change rows, and then posts the message to an Azure Event Hub. Once landed in the Event Hub an Azure Stream Analytics (ASA) Job distributes the changes into the multiple outputs.
What I found pretty cool was that I could transmit SQL delta changes from source to target in as little as 5 seconds end to end!
There are a bunch of steps, but the end result is worth it.
Let’s consider an application that does some real-time stateful stream processing with the Kafka Streams API. We’ll run through a specific example of the end-to-end reference architecture and show you how to:
Run a Kafka source connector to read data from another system (a SQLite3 database), then modify the data in-flight using Single Message Transforms (SMTs) before writing it to the Kafka cluster
Process and enrich the data from a Java application using the Kafka Streams API (e.g. count and sum)
Run a Kafka sink connector to write data from the Kafka cluster to another system (AWS S3)
Read the whole thing.