|
RAR-files |
| rebar-building.batch.data.pipelines.on.gcp.rar |
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EDCD2C26 |
| rebar-building.batch.data.pipelines.on.gcp.r00 |
15,000,000 |
D0727062 |
| rebar-building.batch.data.pipelines.on.gcp.r01 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r02 |
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| rebar-building.batch.data.pipelines.on.gcp.r03 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r04 |
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| rebar-building.batch.data.pipelines.on.gcp.r05 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r06 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r07 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r08 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r09 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r10 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r11 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r12 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r13 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r14 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r15 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r16 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r17 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r18 |
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| rebar-building.batch.data.pipelines.on.gcp.r19 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r20 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r21 |
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| rebar-building.batch.data.pipelines.on.gcp.r22 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r23 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r24 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r25 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r26 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r27 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r28 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r29 |
15,000,000 |
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| rebar-building.batch.data.pipelines.on.gcp.r30 |
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|
| Total size: |
468,354,361 |
|
|
| Archived
files |
|
01 - Introduction\01 - Course Introduction.mp4
[9bbebe4ff74eeb0e]
|
6,354,578 |
4E2B9202 |
|
01 - Introduction\02 - Getting started with GCP and Qwiklabs.mp4
[e86bcc5d467e045a]
|
9,084,962 |
9F649ED1 |
|
02 - Introduction to Batch Data Pipelines\03 - EL, ELT, ETL.mp4
[782a636bcf7deb27]
|
14,616,053 |
3654A087 |
|
02 - Introduction to Batch Data Pipelines\04 - Quality considerations.mp4
[5fb4ba9c0be606f8]
|
4,928,062 |
54C03114 |
|
02 - Introduction to Batch Data Pipelines\05 - How to carry out operations in BigQuery.mp4
[abd94f24088efba4]
|
9,242,778 |
573F7CA5 |
|
02 - Introduction to Batch Data Pipelines\06 - Shortcomings.mp4
[890800bbe2d7a207]
|
10,919,076 |
6314155A |
|
02 - Introduction to Batch Data Pipelines\07 - ETL to solve data quality issues.mp4
[ae42fc5ae2b333cb]
|
13,232,568 |
1C689D75 |
|
03 - Executing Spark on Cloud Dataproc\08 - The Hadoop ecosystem.mp4
[cfaff59b1ba2bda2]
|
21,597,888 |
905A43A4 |
|
03 - Executing Spark on Cloud Dataproc\09 - Running Hadoop on Cloud Dataproc.mp4
[723af0fdea9e5a4c]
|
22,420,365 |
D9C071BB |
|
03 - Executing Spark on Cloud Dataproc\10 - GCS instead of HDFS.mp4
[4101cf049c218b6d]
|
16,543,736 |
84125C9B |
|
03 - Executing Spark on Cloud Dataproc\11 - Optimizing Dataproc.mp4
[c1faaf6e61a9410a]
|
11,711,557 |
32E6DFC8 |
|
03 - Executing Spark on Cloud Dataproc\12 - Optimizing Dataproc Storage.mp4
[50d20da1c97ff3fa]
|
23,832,380 |
07001C22 |
|
03 - Executing Spark on Cloud Dataproc\13 - Optimizing Dataproc Templates and Autoscaling.mp4
[646ea541ea6dccad]
|
10,338,722 |
74F2594D |
|
03 - Executing Spark on Cloud Dataproc\14 - Optimizing Dataproc Monitoring.mp4
[c046b9f9b98dd31f]
|
9,144,760 |
E257BFA8 |
|
03 - Executing Spark on Cloud Dataproc\15 - Lab Intro -Running Apache Spark jobs on Cloud Dataproc.mp4
[2847d49a80d4c350]
|
2,741,414 |
325C094B |
|
03 - Executing Spark on Cloud Dataproc\16 - Running Apache Spark jobs on Cloud Dataproc.mp4
[98ec110f50989b13]
|
261,106 |
385ACA5F |
|
03 - Executing Spark on Cloud Dataproc\17 - Summary.mp4
[aaff45eec8907fd0]
|
2,945,097 |
304D801F |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\18 - Introduction.mp4
[22a9cf4b06380cfe]
|
15,965,426 |
138F045F |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\19 - Components of Data Fusion.mp4
[c23734f9740aa848]
|
7,331,173 |
0C8CD06A |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\20 - Building a Pipeline.mp4
[ba1d2c2b8b765f3b]
|
13,081,296 |
A916FCD1 |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\21 - Exploring Data using Wrangler.mp4
[f669240cd7fa1544]
|
6,681,020 |
9048B29F |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\22 - Lab -Building and executing a pipeline graph in Cloud Data Fusion.mp4
[e13c768167e7aa2f]
|
4,226,492 |
EDE1AC71 |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\23 - Building and Executing a Pipeline Graph with Data Fusion.mp4
[3924135fcc479560]
|
260,627 |
CF32031A |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\24 - Orchestrating work between GCP services with Cloud Composer.mp4
[e787058c63a90697]
|
8,508,915 |
9CB5B7AD |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\25 - Apache Airflow Environment.mp4
[291d085b55fee63e]
|
3,691,990 |
350B76B2 |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\26 - DAGs and Operators.mp4
[3ac781d66ba05180]
|
23,972,888 |
DB66AA47 |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\27 - Workflow scheduling.mp4
[29d5ba8db53ed3e7]
|
17,760,336 |
155D702F |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\28 - Monitoring and Logging.mp4
[47262d464d09bb0e]
|
14,181,184 |
7D6C23B3 |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\29 - Lab -An Introduction to Cloud Composer.mp4
[224cc6309f9cd127]
|
2,692,479 |
CC03D32F |
|
04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer\30 - An Introduction to Cloud Composer.mp4
[c1a11d9492c3821c]
|
250,930 |
F8D64A0E |
|
05 - Serverless Data Processing with Cloud Dataflow\31 - Cloud Dataflow.mp4
[17783b7253d487fc]
|
19,668,794 |
4B27FBEA |
|
05 - Serverless Data Processing with Cloud Dataflow\32 - Why customers value Dataflow.mp4
[48c236e0ba0c721a]
|
11,628,640 |
251AE6B2 |
|
05 - Serverless Data Processing with Cloud Dataflow\33 - Building Cloud Dataflow Pipelines in code.mp4
[726ef40e52a70c20]
|
7,097,965 |
533D3747 |
|
05 - Serverless Data Processing with Cloud Dataflow\34 - Key considerations with designing pipelines.mp4
[553a06e7e7cb6cfb]
|
5,211,519 |
3BFAE154 |
|
05 - Serverless Data Processing with Cloud Dataflow\35 - Transforming data with PTransforms.mp4
[d5b874deb237d843]
|
8,583,103 |
27462AEC |
|
05 - Serverless Data Processing with Cloud Dataflow\36 - Lab -Building a Simple Dataflow Pipeline.mp4
[e2bf7cece3b266fc]
|
1,853,437 |
9D0496F1 |
|
05 - Serverless Data Processing with Cloud Dataflow\37 - Serverless Data Analysis with Dataflow - A Simple Dataflow Pipeline (Java).mp4
[a86d6dac3dd5dbfe]
|
256,835 |
70251D95 |
|
05 - Serverless Data Processing with Cloud Dataflow\38 - Serverless Data Analysis with Dataflow - A Simple Dataflow Pipeline (Python).mp4
[53e7fddb93019188]
|
263,585 |
9A2F0BAB |
|
05 - Serverless Data Processing with Cloud Dataflow\39 - Aggregating with GroupByKey and Combine.mp4
[a9007202e6a6e54c]
|
16,585,176 |
125858A3 |
|
05 - Serverless Data Processing with Cloud Dataflow\40 - Lab -MapReduce in Cloud Dataflow.mp4
[77a312e3f6d1249]
|
1,858,818 |
78925A4D |
|
05 - Serverless Data Processing with Cloud Dataflow\41 - Serverless Data Analysis with Dataflow - MapReduce in Dataflow (Java).mp4
[3732ebe9c6ad3308]
|
262,840 |
AC32DF8B |
|
05 - Serverless Data Processing with Cloud Dataflow\42 - Serverless Data Analysis with Dataflow - MapReduce in Dataflow (Python).mp4
[92f0f900b3278f84]
|
263,908 |
6BC87A6C |
|
05 - Serverless Data Processing with Cloud Dataflow\43 - Side Inputs and Windows of data.mp4
[6acb491cfa5ce409]
|
11,148,549 |
1C03F38F |
|
05 - Serverless Data Processing with Cloud Dataflow\44 - Lab -Practicing Pipeline Side Inputs.mp4
[d976945846fb79c0]
|
1,123,865 |
2DA46BF9 |
|
05 - Serverless Data Processing with Cloud Dataflow\45 - Serverless Data Analysis with Dataflow - Side Inputs (Python).mp4
[f523dd03ff753d40]
|
261,301 |
506B7B6E |
|
05 - Serverless Data Processing with Cloud Dataflow\46 - Serverless Data Analysis with Dataflow - Side Inputs (Java).mp4
[c8fdcd93638619ff]
|
264,918 |
4BCBF10D |
|
05 - Serverless Data Processing with Cloud Dataflow\47 - Creating and re-using Pipeline Templates.mp4
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|
11,552,333 |
DF59F7EB |
|
05 - Serverless Data Processing with Cloud Dataflow\48 - Cloud Dataflow SQL pipelines.mp4
[7024f7d1c80c5cc3]
|
20,113,797 |
2AE1BDC3 |
|
06 - Summary\49 - Course Summary.mp4
[3bea7a2912bcf24d]
|
41,823,551 |
3B971443 |
|
01 - Introduction |
0 |
00000000 |
|
02 - Introduction to Batch Data Pipelines |
0 |
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|
03 - Executing Spark on Cloud Dataproc |
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04 - Manage Data Pipelines with Cloud Data Fusion and Cloud Composer |
0 |
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|
05 - Serverless Data Processing with Cloud Dataflow |
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|
06 - Summary |
0 |
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|
| Total size: |
468,342,792 |
|
|
