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The DataOps Blog

Where Change Is Welcome

Creating a Post-Lambda World with Apache Kudu

By September 23, 2016

Apache Kudu and Open Source StreamSets Data Collector Simplify Batch and Real-Time Processing

As originally posted on the Cloudera VISION Blog.

At StreamSets, we come across dataflow challenges for a variety of applications. Our product, StreamSets Data Collector is an open-source any-to-any dataflow system that ensures that all your data is safely delivered in the various systems of your choice. At its core is the ability to handle data drift that allows these dataflow pipelines to evolve with your changing data landscape without incurring redesign costs.

This position at the front of the data pipeline has given us visibility into various use cases, and we have found that many applications rely on patched-together architectures to achieve their objective.

Ingesting Drifting Data into Hive and Impala

By September 8, 2016

HiveDrift2Importing data into Apache Hive is one of the most common use cases in big data ingest, but gets tricky when data sources ‘drift’, changing the schema or semantics of incoming data. Introduced in StreamSets Data Collector (SDC), the Hive Drift Solution monitors the structure of incoming data, detecting schema drift and updating the Hive Metastore accordingly, allowing data to keep flowing. In this blog entry, I’ll give you an overview of the Hive Drift Solution and explain how you can try it out for yourself, today.

Ingesting Sensor Data on the Raspberry Pi with StreamSets Data Collector

By June 27, 2016

Raspberry Pi with SensorIn the unlikely event you’re not familiar with the Raspberry Pi, it’s an ARM-based computer about the same size as a deck of playing cards. The latest iteration, Raspberry Pi 3, has a 1.2GHz ARMv8 CPU, 1MB of memory, integrated Wi-Fi and Bluetooth, all for the same $35 price tag as the original Raspberry Pi released in 2012. Running a variety of operating systems, including Linux, it’s actually quite a capable little machine. How capable? Well, last week, I successfully ran StreamSets Data Collector (SDC) on one of mine (yes, I have several!), ingesting sensor readings and sending them to Apache Cassandra for analysis.

Here’s how you can build your own Internet of Things testbed and ingest sensor data for around $50.

Visualize StreamSets Data Collector Metrics with Datadog

By June 20, 2016

Datadog LogoBack in January, Adam blogged about StreamSets Monitoring with Grafana, InfluxDB, and jmxtrans. While the Grafana/InfluxDB/jmxtrans open source stack works great, there’s quite a lot of setup and configuration to keep track of. Another tool that StreamSets customers are using to monitor their infrastructure is Datadog; in today’s blog post I’ll show you the basics of configuring Datadog with StreamSets Data Collector (SDC).

Ingesting MQTT Traffic into Riak TS via RabbitMQ and StreamSets

By June 13, 2016

Riak TS LogoRiak KV is an open source, distributed, NoSQL key-value data store oriented towards high availability, fault tolerance and scalability. With its initial release in 2009, Risk KV is in use at companies such as AT&T, Comcast and GitHub. Last October, Basho, the vendor behind Riak KV, announced Riak TS. Riak TS, another distributed, NoSQL data store, is optimized for time series data generated by sources such as IoT sensors. The recent release of Riak TS 1.3 as an open source product under the Apache V2 license got me thinking – how would I get sensor data into Riak TS? There are a range of client libraries, which communicate with the data store via protocol buffers, but connected devices tend to use protocols such as MQTT, an extremely lightweight publish/subscribe messaging transport. StreamSets to the rescue!

Analyzing Salesforce Data with StreamSets, Elasticsearch, and Kibana

By June 3, 2016

UPDATE – Salesforce origin and destination stages, as well as a destination for Salesforce Wave Analytics, were released in StreamSets Data Collector Use the supported, shipping Salesforce stages rather than the unsupported code mentioned below!

After I published a proof-of-concept Salesforce Origin for StreamSets Data Collector (SDC), I noticed an article on the Elastic blog, Analyzing Salesforce Data with Logstash, Elasticsearch, and Kibana. In the blog entry, Elastic systems architect Russ Savage (now at Cask Data), explains the motivation for ingesting Salesforce data into Elasticsearch:

Working directly with sales and marketing operations, we outlined a number of challenges they had that might be solved with this solution. Those included:

  • Interactive time-series snapshot analysis across a number of dimensions. By sales rep, by region, by campaign and more.
  • Which sales reps moved the most pipeline the day before the end of month/quarter? What was the progression of Stage 1 opportunities over time.
  • Correlating data outside of Salesforce (like web traffic) to pipeline building and demand. By region/country/state/city and associated pipeline.

It’s very challenging to look back in time and see trends in the data. Many companies have configured Salesforce to save reporting snapshots, but if you’re like me, you want to see the data behind the aggregate report. I want the ability to drill down to any level of detail, for any timeframe, and find any metric. We found that Salesforce snapshots just aren’t flexible enough for that.

Since we have first-class support for Elasticsearch as a destination in SDC, I decided to recreate the use case with the Salesforce Origin and see if we could fulfill those same requirements while taking advantage of StreamSets’ interactive pipeline IDE and ability to continuously monitor origins for new data.

May the 4th Be With You – Analyzing Star Wars Twitter Mentions in Minecraft

By May 4, 2016

Arena - high angleA couple of weeks ago, as May the 4th approached, a lively Star Wars debate brewed at StreamSets:

  • “Do new school characters get as much play as old favorites like Darth Vader, Yoda and Han Solo?”
  • “Does the Dark Side of the Force dominate the Light?”
  • “Does Yoda prevail over Darth Vader?”

It occurred to us that, with the Twitter Streaming API and StreamSets Data Collector, we didn’t have to guess or debate. We built a data flow that ingested and analyzed tweets and then displayed them in … Minecraft!

Using StreamSets to Ingest Salesforce Data for Analysis

By April 29, 2016 origin allows ingest from SalesforceUPDATE – Salesforce origin and destination stages, as well as a destination for Salesforce Wave Analytics, were released in StreamSets Data Collector Use the supported, shipping Salesforce stages rather than the unsupported code mentioned below!

As I’ve mentioned a couple of times, my previous gig was as a developer evangelist at Salesforce, with particular focus on integration. A few weeks ago, I wrote a custom destination allowing StreamSets Data Collector (SDC) to write data to Salesforce Wave Analytics; today, I’ll show you how to ingest data from Salesforce and write it to any destination supported by SDC.

New Tutorial: Creating a Custom StreamSets Destination

By March 23, 2016

One of the first things I hear after I explain the basics of StreamSets Data Collector is, “Cool, so can I ingest data from/send data to X?”, for varying values of X. The short answer is, “Yes, you can!”, while the longer answer involves checking the lists of origins (for ingesting data from X) and destinations (for writing data) included with the product, and writing custom code if X is not on the list.

image_11“My X isn’t on the list! How do I get started writing that custom code?”, I hear you shout; well, I just wrote a detailed tutorial for creating your first custom StreamSets destination that explains all. Fire up your IDE, follow the steps, and you’ll build a sample destination that sends records to RequestBin, but could be adapted to send them pretty much anywhere.

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