Data In Motion

The main source of this real-time transit updates for New Jersey, New York and Connecticut is TRANSCOM. I will read from this datasource every minute to know about real-time traffic events that occurring on the roads and transportation systems near me. We will be reading the feed that is in XML/RSS format and parse out the hundreds of events that come with each minutes update.

I want to store the raw XML/RSS file in S3/ADLS2/HDFS or GCS, that's an easy step. I will also parse and enhance this data for easier querying and tracking.

I will add to all events a unique ID and a timestamp as the data is streaming by. I will store my data in Impala/Kudu for fast queries and upserts. I can then build some graphs, charts and tables with Apache Hue and Cloudera Visual Applications. I will also publish my data as AVRO enhanced with a schema to Kafka so that I can use it from Spark, Kafka Connect, Kafka Streams and Flink SQL applications.

GenerateFlowFile - optional scheduler
InvokeHTTP - call RSS endpoint
PutHDFS - store raw data to Object or File store on premise or in the cloud via HDFS / S3 / ADLSv2 / GCP / Ozone / ...
QueryRecord - convert XML to JSON
SplitJSON - break out individual events

UpdateAttribute - set schema name
UpdateRecord - generate an add a unique ID and timestamp
UpdateRecord - clean up the point field
UpdateRecord - remove garbage whitespace

PutKudu - upsert new data to our Impala / Kudu table.
RetryFlowFile - retry if network or other connectivity issue.

Send Messages to Kafka

Our flow has delivered many messages to our transcomevents topic as schema attached Apache Avro formatted messages.

SMM links into the Schema Registry and schema for this topic.

We use a schema for validation and as a contract between consumers and producers of these traffic events.

Since events are streaming into our Kafka topic and have a schema, we can query them with Continuous SQL with Flink SQL. We can then run some Continuous ETL.

We could also consume this data with Structured Spark Streaming applications, Spring Boot apps, Kafka Streams, Stateless NiFi and Kafka Connect applications.

We also stored our data in Impala / Kudu for permanent storage, ad-hoc queries, analytics, Cloudera Visualizations, reports, applications and more.

It is very easy to have fast data against our agile Cloud Data Lakehouse.

Source Code

https://github.com/tspannhw/SmartTransit

Resources

FLaNK: Using Apache Kudu As a Cache For FDA Updates

InvokeHTTP: We invoke the RSS feed to get our data.
QueryRecord: Convert RSS to JSON
SplitJson: Split one file into individual records. (Should refactor to ForkRecord)
EvaluateJSONPath: Extract attributes you need.
ProcessGroup for SPL Processing

We call and check the RSS feed frequently and we parse out the records from the RSS(XML) feed and check against our cache. We use Cloudera's Real-Time Datahub with Apache Impala/Apache Kudu as our cache to see if we already received a record for that. If it hasn't arrived yet, it will be added to the Kudu cache and processed.

SPL Processing

We use the extracted SPL to grab a detailed record for that SPL using InvokeHTTP.

We have created an Impala/Kudu table to cache this information with Apache Hue.

We use the LookupRecord to read from our cache.

If we don't have that value yet, we send it to the table for an UPSERT.

We send our raw data as XML/RSS to HDFS for archival use and audits.

We can see the results of our flow in Apache Atlas to see full governance of our solution.

So with some simple NiFi flow, we can ingest all the new updates to DailyMed and not reprocess anything that we already have.

Resources

My Year in Review (2020)

Last year, I thought a few things would be coming in 2020.

What's Coming in 2020

Cloud Enterprise Data Platforms
Hybrid Cloud
Streaming with Flink, Kafka, NiFi
AI at the Edge with Microcontrollers and Small Devices
Voice Data In Queries
Event Handler as a Service (Automatic Kafka Message Reading)
More Powerful Parameter Based Modular Streaming
Cloud First For Big Data
Log Handling Moves to MiNiFi
Full AI At The Edge with Deployable Models
More Powerful Edge TPU/GPU/VPU
Kafka is everywhere
Open Source UI Driven Event Engines
FLaNK Stack gains popularity
FLINK Everywhere

Some of this was deferred due to the global pandemic and I had a big miss with Voice Data and some advances in streaming getting delayed.

Here's a list bit of new news for 2020, SRM was added to Kafka DataHub in the Public Cloud:

https://docs.cloudera.com/runtime/7.2.6/srm-overview/topics/srm-replication-overview.html

I also just did a best of 2020 video you can check out:

Articles from 2020

Talks Around the World (Virtual)

June 11, 2020 - INRHYTHM Lightning Talk

August 13, 2020 - Real-Time Analytics

https://www.cloudera.com/about/events/virtual-events/realworld-streaming-analytics.html

August 28, 2020 - Apache Beam Summit

September 10, 2020 - Future of Data Princeton Meetup - Flink Flank Flink Fest

September 29 - October 1, 2020 - ApacheCon

September 29, 2020 - Apache Streaming Meetup

October 9, 2020 - DevOps Stage

October 22, 2020 - Flink Forward

October 26, 2020 - Open Source Summit Europe

October 27, 2020 - AI Dev World

October 30, 2020 - Nethope Conference with Cloudera Foundation

November 10, 2020 - Variis Talk

https://www.eventbrite.com/e/maximizing-performance-presenting-an-expert-panel-on-fitness-technology-tickets-128156075271#

November 25, 2020 - Big Data Conference

December 14, 2020 - Apache MXNet Day

https://www.eventbrite.com/e/apache-mxnet-day-dec-14th-900-to-500-pm-pst-tickets-127767842055#

Source Code

Slides From Events and Talks 2020

This Year's Devices

NVIDIA Jetson Xavier NX
NVIDIA Jetson Nano 2GB
Breakout Garden for Raspberry Pi (I2C + SPI)
SGP30 Air Quality Sensor Breakout
MLX90640 Thermal Camera Breakout – Wide angle (110°)
Raspberry Pi 4 - 8G RAM!

What's Coming in 2021

AI Driven Streaming
Cloudera Data Platform on GCP
OpenShift Powered by CDP
More Hybrid Cloud
SQL Stream Builder for Flink SQL
More powerful Edge AI
More TPUs, GPUs and specialized chips
More Streaming
More COVID datasets
More Robots
More Automation
More Serverless
Voice Data In Queries
K8 Everywhere...
K8 Backlash

References:

Simple Change Data Capture (CDC) with SQL Selects via Apache NiFi (FLaNK)

Sometimes you need real CDC and you have access to transaction change logs and you use a tool like QLIK REPLICATE or GoldenGate to pump out records to Kafka and then Flink SQL or NiFi can read them and process them.

Other times you need something easier for just some basic changes and inserts to some tables you are interested in receiving new data as events. Apache NiFi can do this easily for you with QueryDatabaseTableRecord, you don't need to know anything but the database connection information, table name and what field may change. NiFi will query, watch state and give you new records. Nothing is hardcoded, parameterize those values and you have a generic Any RDBMS to Any Other Store data pipeline. We are reading as records which means each FlowFile in NiFi can have thousands of records that we know all the fields, types and schema related information for. This can be ones that NiFi infers the schema or ones we use from a Schema Registry like Cloudera's amazing Open Source Schema Registry.

Let's see what data is in our Postgresql table?

How to

QueryDatabaseTableRecord (we will output Json records, but could have done Parquet, XML, CSV or AVRO)
UpdateAttribute - optional - set a table and schema name, can do with parameters as well.
MergeRecord - optional - let's batch these up.
PutORC - let's send these records to HDFS (which could be on bare metal disks, GCS, S3, Azure or ADLS). This will build us an external hive table.

PutORC

As you can see we are looking at the "prices" table and checking maximum values to increment on the updated_on date and the item_id sequential key. We then output JSON records.

We could then:

Add-Ons Examples

PutKudu
PutHDFS (send as JSON, CSV, Parquet) and build an Impala or Hive table on top as external
PutHive3Streaming (Hive 3 ACID Tables)
PutS3
PutAzureDataLakeStorage
PutHBaseRecord
PublishKafkaRecord_2_* - send a copy to Kafka for Flink SQL, Spark Streaming, Spring, etc...
PutBigQueryStreaming (Google)
PutCassandraRecord
PutDatabaseRecord - let's send to another JDBC Datastore
PutDruidRecord - Druid is a cool datastore, check it out on CDP Public Cloud
PutElasticSearchRecord
PutMongoRecord
PutSolrRecord
PutRecord (to many RecordSinkServices like Databases, Kafka, Prometheus, Scripted and Site-to-Site)
PutParquet (store to HDFS as Parquet files)

You can do any number or all of these or multiple copies of each to other clouds or clusters. You can also enrichment, transformation, alerts, queries or routing.

These records can be also manipulated ETL/ELT style with Record processing in stream with options such as:

QueryRecord (use Calcite ANSI SQL to query and transform records and can also change output type)
JoltTransformRecord (use JOLT against any record not just JSON)
LookupRecord (to match against Lookup services like caches, Kudu, REST services, ML models, HBase and more)
PartitionRecord (to break up into like groups)
SplitRecord (to break up record groups into records)
UpdateRecord (update values in fields, often paired with LookupRecord)
ValidateRecord (check against a schema and check for extra fields)
GeoEnrichIPRecord
ConvertRecord (change between types like JSON to CSV)

When you use PutORC, it will give you the details on building your external table. You can do a PutHiveQL to auto-build this table, but most companies want this done by a DBA.

CREATE EXTERNAL TABLE IF NOT EXISTS `pricesorc` (`item_id` BIGINT, `price` DOUBLE, `created_on` BIGINT, `updated_on` BIGINT)
STORED AS ORC
LOCATION '/user/tspann/prices'