Hi Spark Community,
I see that y'all have a logo uploaded to https://www.apache.org/logos/#spark
but it has black text. Is there an official, alternate logo with lighter text
that would look good on a dark background?
Thanks,
Mike
Does anything different happened when you set the isolationLevel to do
Dirty Reads i.e. "READ_UNCOMMITTED"
On Sun, May 24, 2020 at 7:50 PM Manjunath Shetty H
wrote:
> Hi,
>
> We are writing a ETL pipeline using Spark, that fetch the data from SQL
> server in batch mode (every 15mins). Problem
Nandan,
Please send unsubscribe requests to user-unsubscr...@spark.apache.org
On Tue, Nov 26, 2019 at 6:02 AM @Nandan@
wrote:
> unsubscribe
>
parallelism during read operations. Hope
this makes sense.
Thank you
Best Regards,
Mike
and failed with the same error. I even changed to 1MB and
still the same.
Appreciate if you can share any input. Thank you very much.
Best Regards,
MIke
== Physical Plan ==
*(10) Project [product_key#445, store_key#446, fiscal_year#447,
fiscal_month#448, fiscal_week_of_year#449, fiscal_year_week
and failed with the same error. I even changed to 1MB and
still the same.
Appreciate if you can share any input. Thank you very much.
Best Regards,
MIke
== Physical Plan ==
*(10) Project [product_key#445, store_key#446, fiscal_year#447,
fiscal_month#448, fiscal_week_of_year#449, fiscal_year_week
and failed with the same error. I even changed to 1MB and
still the same.
Appreciate if you can share any input. Thank you very much.
Best Regards,
MIke
== Physical Plan ==
*(10) Project [product_key#445, store_key#446, fiscal_year#447,
fiscal_month#448, fiscal_week_of_year#449, fiscal_year_week
I have a question about Spark and how it delegates filters to a Parquet-based
table. I have two tables in Hive in Parquet format. Table1 has with four
columns of type double and table2 has two columns of type double. I am doing an
INNER JOIN of the following:
SELECT table1.name FROM table1
in my map function? Or do you have any recommendations
as to how I could set up a streaming job in a different way that would
allow me to accept metadata on the stream of records coming in and pull
each file down from s3 for processing?
Thanks in advance for your help!
Mike
d a) is faster than c) because a) is limited to sql
query so Spark can do a lot things to optimize (such as not fully
deserialize the objects). But I don't understand b) is much slower
than c) because I assume both requires full deserialization.
Is there anything I can try to improve b)?
Thanks,
Mike
Cool. Thanks a lot in advance.
On Mon, May 22, 2017 at 2:12 PM, Bryan Jeffrey <bryan.jeff...@gmail.com>
wrote:
> Mike,
>
> I have code to do that. I'll share it tomorrow.
>
> Get Outlook for Android <https://aka.ms/ghei36>
>
>
>
>
> On Mon, Ma
ype)?
Thanks,
Mike
=Car, 2=SUV, 3=Wagon)?
2) If I choose String, any penalty in hard drive space or memory?
Thank you!
Mike
++
| id|students|students.age|
+---+++
| 20|[[c,20], [d,10]]|[20, 10]|
| 10|[[a,null], [b,null]]|[null, null]|
+---++----+
It creates a new column "students.age" instead of imputing the val
m the old to the new. But it is kind of
tedious. Any automatic methods?
Thanks,
Mike
-
To unsubscribe e-mail: user-unsubscr...@spark.apache.org
)
streamingContext.start
streamingContext.awaitTermination
It's not actually counting any of the files in the paths, and I know the
paths are valid.
Can someone tell me if this is possible and if so, give me a pointer on how
to fix this?
Thanks,
Mike
in the paths, and I know the
paths are valid.
Can someone tell me if this is possible and if so, give me a pointer on how
to fix this?
Thanks,
Mike
ght?
Thanks,
-Mike
From: Nicholas Hakobian [mailto:nicholas.hakob...@rallyhealth.com]
Sent: Friday, December 30, 2016 5:50 PM
To: Sesterhenn, Mike
Cc: ayan guha; user@spark.apache.org
Subject: Re: Best way to process lookup ETL with Dataframes
Yep, sequential joins is what I have done in the p
data
will result.
Any other thoughts?
From: Nicholas Hakobian <nicholas.hakob...@rallyhealth.com>
Sent: Friday, December 30, 2016 2:12:40 PM
To: Sesterhenn, Mike
Cc: ayan guha; user@spark.apache.org
Subject: Re: Best way to process lookup ETL with Data
need is to join after
the first join fails.
From: ayan guha <guha.a...@gmail.com>
Sent: Thursday, December 29, 2016 11:06 PM
To: Sesterhenn, Mike
Cc: user@spark.apache.org
Subject: Re: Best way to process lookup ETL with Dataframes
How about this -
Hi all,
I'm writing an ETL process with Spark 1.5, and I was wondering the best way to
do something.
A lot of the fields I am processing require an algorithm similar to this:
Join input dataframe to a lookup table.
if (that lookup fails (the joined fields are null)) {
Lookup into some
issues went away.
I do not full understand Scala yet, but you may be able to set one of your
dataframes to null to accomplish the same.
Mike
On Mon, Oct 17, 2016 at 8:38 PM, Mungeol Heo <mungeol@gmail.com> wrote:
> First of all, Thank you for your comments.
> Actually, What I
Hi Mich -
Can you run a filter command on df1 prior to your map for any rows where
p(3).toString != '-' then run your map command?
Thanks
Mike
On Tue, Sep 27, 2016 at 5:06 PM, Mich Talebzadeh <mich.talebza...@gmail.com>
wrote:
> Thanks guys
>
> Actually these are t
redirect over the SSH session to the
remote server. Note that any link that references a non-accessible IP
address can't be reached (though you can also setup putty / SSH as a proxy
to get around that if needed).
Thanks
Mike
On Mon, Sep 19, 2016 at 4:43 AM, Cristina Rozee <rozee.crist...@gmai
p (the thing that usually bites me with CSV conversions).
Assuming these all match to what you want, I'd try mapping the unparsed
date column out to separate fields and try to see if a year field isn't
matching the expected values.
Thanks
Mike
On Thu, Sep 8, 2016 at 8:15 AM, Daniel Lopes <d
/ guid which are generally unique across
all entries assuming enough randomness. Think of the monotonically
increasing id as an auto-incrementing column (with potentially massive gaps
in ids) from a relational database.
Thanks
Mike
On Sun, Sep 4, 2016 at 6:41 PM, Kevin Tran <kevin...@gmail.
ibly with a UDF, to keep
things a little more clear.
Thanks
Mike
On Fri, Aug 26, 2016 at 4:45 PM, Subhajit Purkayastha <spurk...@p3si.net>
wrote:
> So the data in the fcst dataframe is like this
>
>
>
> Product, fcst_qty
>
> A 100
>
> B
in futility.
Thanks
Mike
On Fri, Aug 26, 2016 at 5:14 PM, Michael Gummelt <mgumm...@mesosphere.io>
wrote:
> Run with "-X -e" like the error message says. See what comes out.
>
> On Fri, Aug 26, 2016 at 2:23 PM, Tal Grynbaum <tal.grynb...@gmail.com>
> wrote:
on the layout and what language you're using.
Thanks
Mike
On Fri, Aug 26, 2016 at 3:29 PM, Subhajit Purkayastha <spurk...@p3si.net>
wrote:
> Mike,
>
>
>
> The grains of the dataFrame are different.
>
>
>
> I need to reduce the forecast qty (which is in the FCST DF)
Without seeing the makeup of the Dataframes nor what your logic is for
updating them, I'd suggest doing a join of the Forecast DF with the
appropriate columns from the SalesOrder DF.
Mike
On Fri, Aug 26, 2016 at 11:53 AM, Subhajit Purkayastha <spurk...@p3si.net>
wrote:
> I am using
Is this what you're after?
def padString(id: Int, chars: String, length: Int): String =
chars * length + id.toString
padString(123, "0", 10)
res4: String = 00123
Mike
On Thu, Aug 25, 2016 at 12:39 PM, Mich Talebzadeh <mich.talebza...@gmail.com
> wrote:
> Thank
"$c%010.2f"
// Result is 123.87
You can also do inline operations on the values before formatting. I've
used this specifically to pad for hex digits from strings.
val d = "100"
val hexstring = f"0x${d.toInt}%08X"
// hexstring is 0x0064
Thanks
Mike
On Thu,
Assuming you know the number of elements in the list, this should work:
df.withColumn('total', df["_1"].getItem(0) + df["_1"].getItem(1) +
df["_1"].getItem(2))
Mike
On Mon, Aug 15, 2016 at 12:02 PM, Javier Rey <jre...@gmail.com> wrote:
> Hi everyone,
&
and referencing it that way so there's less cluster
communication involved. Honestly I doubt there's a lot of variance with
this small of a value but it's a good habit to get into.
Thanks
Mike
On Fri, Aug 5, 2016 at 11:33 AM, Mich Talebzadeh <mich.talebza...@gmail.com>
wrote:
> Th
Should be pretty much the same code for Scala -
import java.util.UUID
UUID.randomUUID
If you need it as a UDF, just wrap it accordingly.
Mike
On Fri, Aug 5, 2016 at 11:38 AM, Mich Talebzadeh <mich.talebza...@gmail.com>
wrote:
> On the same token can one generate a UUID like belo
Not that I've seen, at least not in any worker independent way. To guarantee
consecutive values you'd have to create a udf or some such that provided a new
row id. This probably isn't an issue on small data sets but would cause a lot
of added communication on larger clusters / datasets.
Mike
for that.
I've been toying with an implementation that allows you to specify the split
for better control along with a start value.
Thanks
Mike
> On Aug 5, 2016, at 11:07 AM, Tony Lane <tonylane@gmail.com> wrote:
>
> Mike.
>
> I have figured how to do this . Than
ns library.
Mike
> On Aug 5, 2016, at 9:11 AM, Tony Lane <tonylane@gmail.com> wrote:
>
> Ayan - basically i have a dataset with structure, where bid are unique string
> values
>
> bid: String
> val : integer
>
> I need unique int values for these string bid
This is a little ugly, but it may do what you're after -
df.withColumn('total', expr("+".join([col for col in df.columns])))
I believe this will handle null values ok, but will likely error if there
are any string columns present.
Mike
On Thu, Aug 4, 2016 at 8:41 AM, Javie
things on one of the YARN nodes would seem to
indicate this isn't the case since the spawned daemon gets a separate
process ID and process group, but I wanted to check to confirm as it could
make a big difference to pyspark users hoping to tune things.
Thanks,
Mike
Rui [mailto:sunrise_...@163.com]
Sent: 17 May 2016 11:32
To: Mike Lewis
Cc: user@spark.apache.org
Subject: Re: SparkR query
Lewis,
1. Could you check the values of “SPARK_HOME” environment on all of your worker
nodes?
2. How did you start your SparkR shell?
On May 17, 2016, at 18:07, Mike Lewis
tory
Is this a configuration setting that I’m missing, the worker nodes (linux)
shouldn’t be looking in the spark home of the driver (windows) ?
If so, I’d appreciate someone letting me know what I need to ch
analysis in you language of choice;
hopefully it can aid in finer grained debugging (the headers of the
fields it prints are listed in one of the functions).
Mike
On 4/25/16, Raghava Mutharaju <m.vijayaragh...@gmail.com> wrote:
> Mike,
>
> We ran our program with 16, 32 an
,
but if not and your executors all receive at least *some* partitions, then
I still wouldn't rule out effects of scheduling delay. It's a simple test,
but it could give some insight.
Mike
his could still be a scheduling If only one has *all* partitions, and
email me the log file? (If it's 10+ MB
by unusual initial task scheduling. I don't know of ways to avoid
this other than creating a dummy task to synchronize the executors, but
hopefully someone from there can suggest other possibilities.
Mike
On Apr 23, 2016 5:53 AM, "Raghava Mutharaju" <m.vijayaragh...@gmail.com>
durations less than the initial
executor delay. I recommend you look at your logs to verify if this is
happening to you.
Mike
On 4/18/16, Anuj Kumar <anujs...@gmail.com> wrote:
> Good point Mike +1
>
> On Mon, Apr 18, 2016 at 9:47 AM, Mike Hynes <91m...@gmail.com> wrote:
>
3. Make the tasks longer, i.e. with some silly computational work.
Mike
On 4/17/16, Raghava Mutharaju <m.vijayaragh...@gmail.com> wrote:
> Yes its the same data.
>
> 1) The number of partitions are the same (8, which is an argument to the
> HashPartitioner). In the first case,
alf the number of partitions with the
shuffle flag set to true. Would that be reasonable?
Thank you very much for your time, and I very much hope that someone
from the dev community who is familiar with the scheduler may be able
to clarify the above observations and questions.
Thanks,
Mike
P.S. Ko
f anyone else has any other ideas or experience, please let me know.
Mike
On 4/4/16, Koert Kuipers <ko...@tresata.com> wrote:
> we ran into similar issues and it seems related to the new memory
> management. can you try:
> spark.memory.useLegacyMode = true
>
> On Mo
make a previously (<=1.5) correct configuration go haywire?
Have new configuration settings been added of which I'm unaware that
could lead to this problem?
Please let me know if others in the community have observed this, and
thank yo
Thanks Silvio, JIRA submitted
https://issues.apache.org/jira/browse/SPARK-14332.
On Fri, 25 Mar 2016 at 12:46 Silvio Fiorito <silvio.fior...@granturing.com>
wrote:
> Hi Mike,
>
> Sorry got swamped with work and didn’t get a chance to reply.
>
> I misunderstood what you
Pinging again - any thoughts?
On Wed, 23 Mar 2016 at 09:17 Mike Sukmanowsky <mike.sukmanow...@gmail.com>
wrote:
> Thanks Ted and Silvio. I think I'll need a bit more hand holding here,
> sorry. The way we use ES Hadoop is in pyspark via
> org.elasticsearch.hadoop.mr
o.fior...@granturing.com>
wrote:
> Hi Mike,
>
> It’s been a while since I worked on a custom Source but I think all you
> need to do is make your Source in the org.apache.spark package.
>
> Thanks,
> Silvio
>
> From: Mike Sukmanowsky <mike.sukmanow...@gmail.com&
se the metric sources and sinks described here:
> http://spark.apache.org/docs/latest/monitoring.html#metrics
>
> If you want to push the metrics to another system you can define a custom
> sink. You can also extend the metrics by defining a custom source.
>
> From: Mike Sukmanow
cs framework similar to Hadoop's
Counter system to Spark or is there an alternative means for us to grab
metrics exposed when using Hadoop APIs to load/save RDDs?
Thanks,
Mike
Has anyone used Spark Job Server on a "kerberized" cluster in YARN-Client
mode? When Job Server contacts the YARN resource manager, we see a "Cannot
impersonate root" error and am not sure what we have misconfigured.
Thanks.
___
*Mike Wright*
Principal
Kerberos seems to be working otherwise ... for example, we're using it
successfully to control access to HDFS and it's linked to AD ... we're
using Ranger if that helps. I'm not a systems admin guy so this is really
not my area of expertise.
___
*Mike Wright*
Principal Architect
Thanks for the insight!
___
*Mike Wright*
Principal Architect, Software Engineering
S Capital IQ and SNL
434-951-7816 *p*
434-244-4466 *f*
540-470-0119 *m*
mwri...@snl.com
On Fri, Dec 11, 2015 at 2:38 PM, Michael Armbrust <mich...@databricks.com>
wrote:
> The way t
As part of our implementation, we are utilizing a full "Kerberized" cluster
built on the Hortonworks suite. We're using Job Server as the front end to
initiate short-run jobs directly from our client-facing product suite.
1) We believe we have configured the job server to start with the
Somewhat related - What's the correct implementation when you have a single
cluster to support multiple jobs that are unrelated and NOT sharing data? I
was directed to figure out, via job server, to support "multiple contexts"
and explained that multiple contexts per JVM is not really supported.
at the end and writing them all at once.
I am using a groupBy against the filtered RDD the get the grouping I want,
but apparently this may not be the most efficient way, and it seems that
everything is always in a single partition under this scenario.
___
*Mike Wright*
Principal Architect
idtest/id
goals
goaltest/goal
/goals
/execution
/executions
/plugin
/plugins
On Tue, Aug 25, 2015 at 2:10 PM, Mike Trienis mike.trie...@orcsol.com
wrote:
Hello
org.apache.spark.sql.hive.test.TestHiveContext
However, it suffers from the same memory issue. Has anyone else suffered
from the same problem? Note that I am running these unit tests on my mac.
Cheers, Mike.
a single
PySpark context?
--
Mike Sukmanowsky
Aspiring Digital Carpenter
*e*: mike.sukmanow...@gmail.com
LinkedIn http://www.linkedin.com/profile/view?id=10897143 | github
https://github.com/msukmanowsky
with:
col2 | col3
---
item_1 | 2
item_2 | 2
Thanks, Mike.
(entity: Entity): EntityExtended = {
val id = entity.identifier
// lookup identifier in broadcast variable?
}
}
Thanks, Mike.
Hi,
Does anyone know how I could control the number of reducer when we do operation
such as groupie For data frame?
I could set spark.sql.shuffle.partitions in sql but not sure how to do in
df.groupBy(XX) api.
Thanks,
Mike
doesn't finish.
All I see in the log is this :
Can someone point me , to what I'm doing wrong ?
Thanks,
Mike
--
View this message in context:
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Sent from the Apache Spark User
Definitely, thanks Mohammed.
On Mon, Jul 20, 2015 at 5:47 PM, Mohammed Guller moham...@glassbeam.com
wrote:
Thanks, Harish.
Mike – this would be a cleaner version for your use case:
df.filter(df(filter_field) === value).select(field1).show()
Mohammed
*From:* Harish Butani
I wanted to ask a general question about Hadoop/Yarn and Apache Spark
integration. I know that
Hadoop on a physical cluster has rack awareness. i.e. It attempts to minimise
network traffic
by saving replicated blocks within a rack. i.e.
I wondered whether, when Spark is configured to use
= value);
- df.select(field1).filter(df(filter_field) === value).show()
As a work-around, it seems that I can do the following
- df.select(field1, filter_field).filter(df(filter_field) ===
value).drop(filter_field).show()
Thanks, Mike.
transformations.
Regards
Mike
: value split is not a member of Nothing
df1.explode(mv,mvnew)(mv = mv.split(,))
Am i doing something wrong? I need to extract data under mi.mv in separate
columns so i can apply some transformations.
Regards
Mike
aware that I can play with scala and get around this issue and I have
but I wondered whether others
have come across this and solved it ?
cheers
Mike F
.
Has any one else run into this problem before and how did you solve it?
Thanks, Mike.
-
To unsubscribe, e-mail: user-unsubscr...@spark.apache.org
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Hi Das,
Thanks for your reply. Somehow I missed it..
I am using Spark 1.3. The data source is from kafka.
Yeah, not sure why the delay is 0. I'll run against 1.4 and give a screenshot.
Thanks,
Mike
From: Akhil Das ak...@sigmoidanalytics.commailto:ak...@sigmoidanalytics.com
Date: Thursday, June
Hi
Is it true that if I want to use Spark SQL ( for Spark 1.3.1 ) against Apache
Hive I need to build a source version of Spark ?
Im using CDH 5.3 on CentOS Linux 6.5 which uses Hive 0.13.0 ( I think ).
cheers
Mike F
? If this is the case, the scheduling delay should be
high rather than 0. Am I missing anything?
Thanks,
Mike
? If this is the case, the scheduling delay should be high
rather than 0. Am I missing anything?
Thanks,
Mike
()
dfHashTags.registerTempTable(tweets)
}
// extra stuff here
ssc.start()
ssc.awaitTermination()
} // end main
} // end twitter1
cheers
Mike F
/30672648/how-to-autostart-an-apache-spark-cluster-using-supervisord/30676844#30676844
Cheers Mike
On Wed, Jun 3, 2015 at 12:29 PM, Igor Berman igor.ber...@gmail.com wrote:
assuming you are talking about standalone cluster
imho, with workers you won't get any problems and it's straightforward
the cluster.
I am considering using supervisord to control all the processes (worker,
master, ect.. ) in order to have the cluster up an running after boot-up;
although I'd like to understand if it will cause more issues than it
solves.
Thanks, Mike.
core, an executor is simply a jvm instance and
as such it can be granted any number of cores and ram
So check how many cores you have per executor
Sent from Samsung Mobile
Original message
From: Mike Trienis
Date:2015/05/22 21:51 (GMT+00:00)
To: user@spark.apache.org
)
result.foreachPartition { i =
i.foreach(record = connection.insert(record))
}
}
def doSomething(rdd: RDD[Data]) : RDD[MyObject] = {
rdd.flatMap(MyObject)
}
Any ideas as to how to improve the throughput?
Thanks, Mike.
I guess each receiver occupies a executor. So there was only one executor
available for processing the job.
On Fri, May 22, 2015 at 1:24 PM, Mike Trienis mike.trie...@orcsol.com
wrote:
Hi All,
I have cluster of four nodes (three workers and one master, with one core
each) which consumes data
Hi
Im getting the following error when trying to process a csv based data file.
Exception in thread main org.apache.spark.SparkException: Job aborted due to
stage failure: Task 1 in stage 10.0 failed 4 times, most recent failure: Lost
task 1.3 in stage 10.0 (TID 262,
Hey Chris!
I was happy to see the documentation outlining that issue :-) However, I
must have got into a pretty terrible state because I had to delete and
recreate the kinesis streams as well as the DynamoDB tables.
Thanks for the reply, everything is sorted.
Mike
On Fri, May 8, 2015 at 7
. If you see errors, you may need to manually delete
the DynamoDB table.*
On Fri, May 8, 2015 at 2:06 PM, Mike Trienis mike.trie...@orcsol.com
wrote:
Hi All,
I am submitting the assembled fat jar file by the command:
bin/spark-submit --jars /spark-streaming-kinesis-asl_2.10-1.3.0.jar
to be outputting the data.
Has anyone else encountered a similar issue? Does spark cache the stream
name somewhere? I also have checkpointing enabled as well.
Thanks, Mike.
with no success :(
Would be curious to know if you got it working.
Vadim
On Apr 13, 2015, at 9:36 PM, Mike Trienis mike.trie...@orcsol.com wrote:
Hi All,
I have having trouble building a fat jar file through sbt-assembly.
[warn] Merging 'META-INF/NOTICE.txt' with strategy 'rename'
[warn
-assembly-0.1-SNAPSHOT.jar
Thanks again Richard!
Cheers Mike.
On Tue, Apr 14, 2015 at 11:01 AM, Richard Marscher rmarsc...@localytics.com
wrote:
Hi,
I've gotten an application working with sbt-assembly and spark, thought
I'd present an option. In my experience, trying to bundle any
as a *provided *dependency?
Also, is there a merge strategy I need apply?
Any help would be appreciated, Mike.
got it working.
Vadim
On Apr 13, 2015, at 9:36 PM, Mike Trienis mike.trie...@orcsol.com wrote:
Hi All,
I have having trouble building a fat jar file through sbt-assembly.
[warn] Merging 'META-INF/NOTICE.txt' with strategy 'rename'
[warn] Merging 'META-INF/NOTICE' with strategy 'rename
'
Michael
On Mon, Apr 13, 2015 at 10:13 AM, pprett [via Apache Spark User List]
ml-node+s1001560n22470...@n3.nabble.com wrote:
Hi Mike,
Gradient Boosted Trees (or gradient boosted regression trees) dont store
probabilities in each leaf node but rather model a continuous function
which
It's because your tests are running in parallel and you can only have one
context running at a time.
--
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Sent from the Apache Spark User List mailing list archive at
on a proper deployment, and will be sure to share my findings.
Thanks, Mike!
On Sat, Mar 21, 2015 at 8:09 AM, Chris Fregly ch...@fregly.com wrote:
hey mike!
you'll definitely want to increase your parallelism by adding more shards
to the stream - as well as spinning up 1 receiver per shard
, HDFS or any other
Hadoop-supported file system. Spark will call toString on each element to
convert it to a line of text in the file.
Thanks, Mike.
Please ignore my question, you can simply specify the root directory and it
looks like redshift takes care of the rest.
copy mobile
from 's3://BUCKET_NAME/'
credentials
json 's3://BUCKET_NAME/jsonpaths.json'
On Thu, Mar 5, 2015 at 3:33 PM, Mike Trienis mike.trie...@orcsol.com
wrote:
Hi
/docs/1.2.0/api/scala/index.html#org.apache.spark.streaming.dstream.DStream
Anyone have any ideas?
Thanks, Mike.
it make more sense to
push data from AWS Kinesis to AWS Redshift VIA another standalone approach
such as the AWS Kinesis connectors.
Thanks, Mike.
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