The next step in building this pipeline is to configure the sink or destination for the imported data. Hydrator provides capabilities to store data in time-partitioned directories via a built-in CDAP Dataset called Time-partitioned File Set. Once the data is stored in the fileset, CDAP automatically adds a partition which can be queried using Hive.
In this use case we will configure a Time-partitioned File Set that stores data in Avro format by usingTPFSAvro as the sink.
I like the fact that there’s a UI for this. Between this tool and NiFi, the Hadoop ecosystem is getting some tools to make data migration easier to understand, and I think that will help adoption.
Assume that we have a YARN cluster with total resources <memory: 800GB, vcores 200> with two queues:
root.sometimes_busy(weight 3.0). There are generally four scenarios of interest:
Scenario A: The busy queue is full with applications, and
sometimes_busyqueue has a handful of running applications (say 10%, i.e. <memory: 80GB, vcores: 20>). Soon, a large number of applications are added to the
sometimes_busyqueue in a relatively short time window. All the new applications in
sometimes_busywill be pending, and will become active as containers finish up in the
busyqueue. If the tasks in the
busyqueue are fairly short-lived, then the applications in the
sometimes_busyqueue will not wait long to get containers assigned. However, if the tasks in the
busyqueue take a long time to finish, the new applications in the
sometimes_busyqueue will stay pending for a long time. In either case, as the applications in the
sometimes_busyqueue become active, many of the running applications in the
busyqueue will take much longer to finish.
If you’re interested in a deeper dive into YARN, this is a good series to start with.
When you start using Hive you may miss some of the functions you are used to from Oracle, MySQL or elsewhere. Or you might just want a profanity filter. Whatever the case you can browse our list below for a large selection of UDF libraries. You can also use the pointers listed to write your own.
Coming from a SQL Server background, UDFs might be something you instinctively avoid (or at least that’s the case with me). In practice, though, they’re a really good addition to the product.
This problem is easy to solve, right? You can write scripts that run jobs in sequence, and use the output of one program as the input to another—no problem. But what if your workflow is complex and requires specific triggers, such as specific data volumes or resource constraints, or must meet strict SLAs? What if parts of your workflow don’t depend on each other and can be run in parallel?
Building your own infrastructure around this problem can seem like an attractive idea, but doing so can quickly become laborious. If, or rather when, those requirements change, modifying such a tool isn’t easy . And what if you need monitoring around these jobs? Monitoring requires another set of tools and headaches.
This is a pretty detailed look at the basics of Oozie.
Scheduled Backups and Restore to any cluster: HBase data is backed up to S3 regularly, and you can use Qubole’s cluster management UI to easily configure schedules for your backups. All the data or specific tables can be restored to any other HBase cluster. This feature is not only useful for disaster recovery but also to bring up test or dev clusters as well as clusters for one-off analysis.
Cluster Management: HBase clusters in QDS are elastic! Our users regularly increase the size of the capacity of the clusters. This is a boon for capacity planners as they can be more reactive to workload patterns saving time and money.
Zero impact node rotations: By carefully orchestrating data compaction and HDFS block transfers prior to reassigning region servers, QDS minimizes the impact of adding/deleting/replacing nodes in a HBase cluster.
High-Availability Configurations: Putting together a high availability HBase cluster in the cloud can be daunting. QDS provides prebuilt templates for deploying HA HBase clusters through a single click.
The value proposition for this is very similar to Azure SQL Database: eliminate some of the administrative tasks by accepting their defaults. Doing this does remove some of the complexity of HBase.
With the emergence of Spark as a unified computing engine, developers can perform ETL and advanced analytics in both continuous (streaming) and batch mode either programmatically (using Scala, Java, Python, or R) or with procedural SQL (using Spark SQL or Hive QL).
With MapR converging the data management platform, you can now take a preferential Spark-first approach. This differs from the traditional approach of starting with extended Hadoop tools and then adding Spark as part of your big data technology stack. As a unified computing engine, Spark can be used for faster batch ETL and analytics (with Spark core instead of MapReduce and Hive), machine learning (with Spark MLlib instead of Mahout), and streaming ETL and analytics (with Spark Streaming instead of Storm).
MapReduce is so 2012…
This tools extends IntelliJ to support Spark job life cycle from create, author, debug and submit job to Azure cluster and view results. This IntelliJ HDInsight tool integrates well with Azure to allow user navigate HDInsight Spark clusters and view associated Azure storage account. To further boost productivity, the IntelliJ HDInsight tool also offers the capability to view Spark job history, display detailed job logs, and the job output to boost developer productivity. A few usability improvements have been implemented upon user preview feedback, which includes auto locate artifact, add intelligence to remember assembly location, caches spark logs, etc.
It looks like this is specifically designed for Spark-enabled clusters.
Typically, data warehousing and ETL tool vendors recommended that we write your own custom components. After all, the target market for ETL tools is a space where the tools are specifically marketed as reducing the need for “error prone and time consuming” manual coding. When I ran across this tutorial on writing your own NiFi processor it occurred to me that NiFi is the exact opposite. It’s both Open Source and designed for extensibility from the ground up. I found it quite reasonable to write a custom NiFi processor that leverages our existing code base.
The existing code is a Java program with separate classes for each device vendor, all with the same interface to abstract the nuances of each vendor from the main data export program. This interface follows a traditional paradigm: login, query, query, query, logout. Given that my input to NiFi above takes in simple username, password, and query criteria arguments, it seems trivial to create a NiFi processor class that adapts the existing code into the NiFi API. Here’s a slightly abbreviated version of the actual code. (In reality, it’s all of 70 lines of code.)
In almost any realistic scenario, you’re not going to have the opportunity to start from scratch. You will always have legacy components, external dependencies, and existing user bases to satisfy. I like this article because it moves forward from that starting point.
As you probably know, if you prepend the word EXPLAIN to your SQL query and then run it, Hive prints out a text description of the query plan. This lets you explore the effects such variations as code changes, the use of analyze, turning on/off the cost-based optimizer (CBO), and so on. It’s an essential tool for optimizing Hive.
The output of EXPLAIN is far from pretty, but fortunately, a simple pipeline of Linux commands can give you a slick graphical rendition like the one below.
I’m going to have to keep this in mind.
Jay Kreps, the co-creator of Apache Kafka and CEO of Confluent, was one of the first big data architects to espouse an alternative to the Lambda architecture, which he did with his 2014 O’Reilly story “Questioning the Lambda Architecture.” While Kreps appreciated some aspects of the Lambda architecture—in particular how it deals with reprocessing data—he stated that the downside was just too great.
“The Lambda architecture says I have to have Hadoop and I have to have Storm and I’m going to implement everything in both places and keep them in sync. “I think that’s extremely hard to do,” Kreps tells Datanami. “I think one of the biggest things hurting stream processing is the amount of complexity that you have to incur to build something. That makes it slow to build applications that way, hard to roll them out, and hard to make them reliable enough to be a key part of the business.
I wonder if we’re seeing the next generation of Kimball v Inmon here, or if one will absolutely dominate.