How Netflix does data in AWS

Separate data : stuff in motion vs stuff at rest. Use bunch of latest open source and aws tools.

https://netflixtechblog.com/byte-down-making-netflixs-data-infrastructure-cost-effective-fee7b3235032

GCP for AWS professionals

Someone made a GCP lookup list for AWS cloud people

Service comparisons

The following table provides a side-by-side comparison of the various services available on AWS and Google Cloud.

Service Category	Service	AWS	Google Cloud
Compute	IaaS	Amazon Elastic Compute Cloud	Compute Engine
	PaaS	AWS Elastic Beanstalk	App Engine
	FaaS	AWS Lambda	Cloud Functions
Containers	CaaS	Amazon Elastic Kubernetes Service, Amazon Elastic Container Service	Google Kubernetes Engine
	Containers without infrastructure	AWS Fargate	Cloud Run
	Container registry	Amazon Elastic Container Registry	Container Registry
Networking	Virtual networks	Amazon Virtual Private Cloud	Virtual Private Cloud
	Load balancer	Elastic Load Balancer	Cloud Load Balancing
	Dedicated interconnect	AWS Direct Connect	Cloud Interconnect
	Domains and DNS	Amazon Route 53	Google Domains, Cloud DNS
	CDN	Amazon CloudFront	Cloud CDN
	DDoS firewall	AWS Shield, AWS WAF	Google Cloud Armor
Storage	Object storage	Amazon Simple Storage Service	Cloud Storage
	Block storage	Amazon Elastic Block Store	Persistent Disk
	Reduced-availability storage	Amazon S3 Standard-Infrequent Access, Amazon S3 One Zone-Infrequent Access	Cloud Storage Nearline and Cloud Storage Coldline
	Archival storage	Amazon Glacier	Cloud Storage Archive
	File storage	Amazon Elastic File System	Filestore
	In-memory data store	Amazon ElastiCache for Redis	Memorystore
Database	RDBMS	Amazon Relational Database Service, Amazon Aurora	Cloud SQL, Cloud Spanner
	NoSQL: Key-value	Amazon DynamoDB	Firestore, Cloud Bigtable
	NoSQL: Indexed	Amazon SimpleDB	Firestore
	In-memory data store	Amazon ElastiCache for Redis	Memorystore
Data analytics	Data warehouse	Amazon Redshift	BigQuery
	Query service	Amazon Athena	BigQuery
	Messaging	Amazon Simple Notification Service, Amazon Simple Queueing Service	Pub/Sub
	Batch data processing	Amazon Elastic MapReduce, AWS Batch	Dataproc, Dataflow
	Stream data processing	Amazon Kinesis	Dataflow
	Stream data ingest	Amazon Kinesis	Pub/Sub
	Workflow orchestration	Amazon Data Pipeline, AWS Glue	Cloud Composer
Management tools	Deployment	AWS CloudFormation	Cloud Deployment Manager
	Cost management	AWS Budgets	Cost Management
Operations	Monitoring	Amazon CloudWatch	Cloud Monitoring
	Logging	Amazon CloudWatch Logs	Cloud Logging
	Audit logging	AWS CloudTrails	Cloud Audit Logs
	Debugging	AWS X-Ray	Cloud Debugger
	Performance tracing	AWS X-Ray	Cloud Trace
Security & identity	IAM	Amazon Identity and Access Management	Cloud Identity and Access Management
	Secret management	AWS Secrets Manager	Secret Manager
	Encrypted keys	AWS Key Management Service	Cloud Key Management Service
	Resource monitoring	AWS Config	Cloud Asset Inventory
	Vulnerability scanning	Amazon Inspector	Web Security Scanner
	Threat detection	Amazon GuardDuty	Event Threat Detection (beta)
	Microsoft Active Directory	AWS Directory Service	Managed Service for Microsoft Active Directory
Machine learning	Speech	Amazon Transcribe	Speech-to-Text
	Vision	Amazon Rekognition	Cloud Vision
	Natural Language Processing	Amazon Comprehend	Cloud Natural Language API
	Translation	Amazon Translate	Cloud Translation
	Conversational interface	Amazon Lex	Dialogflow Enterprise Edition
	Video intelligence	Amazon Rekognition Video	Video Intelligence API
	Auto-generated models	Amazon SageMaker Autopilot	AutoML
	Fully managed ML	Amazon SageMaker	AI Platform
Internet of Things	IoT services	Amazon IoT	Cloud IoT

Modern Data Engineering is Complicated

Modern Data Engineering is Complicated.
There are so many things to know to be good.
Languages : SQL , Python, Scala
Operating Systems : Linux, bash shell
Cloud : AWS, Azure, GCP
Data Pipelines : Airflow, Kubeflow
DevOps : Kubernetes, Docker, VPCs, IAM etc.
Relational Database : PostGres, Mysql, Sql Server
MPP Databases : Redshift, Google Big Query, Snowflake
Big Data Storage : S3, HDFS, Google storage
Data Architecture : Machine Learning models, data warehouse models
Streaming Data : Kafka, Kinesis Firebase, Flink, Storm
No SQL: Dynodb , Mongo
Business Intelligence related : Tableau, Looker, Sisense
Data Lakes : S3 or hdfs or databricks.

 

My other hobby is Stocks

My other hobby is investing and picking stocks.
Here’s my other blog: http://hunandelightmd.com/

How do I unlock blocking queries in Amazon Redshift?

I noticed my dimension table was stuck when I query it for etl job and the truncate statement fails to return.

“Locking is a protection mechanism that controls how many sessions can access a table at the same time and which operations can be performed in those sessions. Most relational databases use row-level locks. Amazon Redshift uses table-level locks. You might experience locking conflicts if you perform frequent DDL operations on user tables, such as ALTER, DROP or TRUNCATE, or DML changes such as UPDATE, DELETE, or INSERT. ”

I use this handy check what’s the locking blocked pids with this code:

use the blocking pid column from this output to find the jobs to kill.

select a.txn_owner, a.txn_db, a.xid, a.pid, a.txn_start, a.lock_mode, a.relation as table_id,nvl(trim(c."name"),d.relname) as tablename, a.granted,b.pid as blocking_pid ,datediff(s,a.txn_start,getdate())/86400||' days '||datediff(s,a.txn_start,getdate())%86400/3600||' hrs '||datediff(s,a.txn_start,getdate())%3600/60||' mins '||datediff(s,a.txn_start,getdate())%60||' secs' as txn_duration
from svv_transactions a 
left join (select pid,relation,granted from pg_locks group by 1,2,3) b 
on a.relation=b.relation and a.granted='f' and b.granted='t' 
left join (select * from stv_tbl_perm where slice=0) c 
on a.relation=c.id 
left join pg_class d on a.relation=d.oid
where  a.relation is not null;

select pg_terminate_backend(blocking_PID);

Remember amazon redshift locks the whole table. 

source:

https://aws.amazon.com/premiumsupport/knowledge-center/prevent-locks-blocking-queries-redshift/

Creating a Tableau Report level View Filter (1 report varying view depending on tableau user logged in)

Creating a Report level View Filter in Tableau (1 report varying view depending on tableau user logged in)

use case:

You want a report to show an employee’s daily sales and for the employee to only see his data and manager to see all his employees’ data.

1. create mapping table with user_manager along with the daily sales data

table 1: user_manager

user_name, manager_name,
mike_s, boss_a
mike_s, mike_s

table 2: user_sales

date, sales_amt, user_name

2018/03/04, $20, mike_s
2018/03/03, $20, mike_s

join two objects together in tableau or before it.

select a.user_name, a.manager_name, b.date, b._sales_amt from user_manager a join user_sales b on a.user_name= b.user_name

now we have:   user_name, manager_name, date, sales_amt

2. create tableau to import the entire object of two tables.

3. create calculated field called permission similar. *most important*
with formula username() = manager_name

4. add and apply permissions filter to tableau workbook

source of this example:

https://onlinehelp.tableau.com/current/pro/desktop/en-us/publish_userfilters_create.html

Deep Learning versus Machine Learning in One Picture

Found on: http://www.datasciencecentral.com/  and

https://www.linkedin.com/pulse/logistic-regression-vs-deep-neural-networks-david-young/

Sometimes a simple regression model will do.

mysql tricks: do instant table swap to mitigate mysql deadlock error.

A deadlock in MySQL happens when two or more transactions mutually hold and request for locks, creating a cycle of dependencies. … InnoDB automatically detects transaction deadlocks, rollbacks a transaction immediately and returns an error. It uses a metric to pick the easiest transaction to rollback. MyISAM engine locks the entire table from read/write requests.

For example you have a near realtime table that you need to update via etl every 10 min , and that same table is used by your clients to read the current status of say batch processes status.

Avoid the mysql deadlock error by re-loading table in near-realtime. First to check if your tables are locked run as root or db admin :

show processlist ;

How do you write to a table while someone’s reading it without deadlock? Simple: load a staging table of the same table structure and do a rename swap in mysql.

Here’s the steps in list format

1. create a table with the same structure of the original one.
2. have etl load that table instead.
3. do some data validity checks.
4. do rename table swap code:  a->c, b->a, c->b  (a = prod, b = stage, c=temp)

example mysql code:

1. create table current_status_stage like  current_status_stage;

2. — load table from source system via mysql command line or otherwise. ex csv

3. LOAD DATA INFILE "/tmp/current_status.csv"
   INTO TABLE current_status_stage
   COLUMNS TERMINATED BY ','
   OPTIONALLY ENCLOSED BY '"'
   ESCAPED BY '"'
   LINES TERMINATED BY '\n';

4. RENAME TABLE reporting.current_status TO reporting.current_status_old, reporting.current_status_stage To reporting.current_status;RENAME TABLE reporting.current_status_old TO reporting.current_status_stage

Improve Amazon Redshift table performance fast & easy.

Assuming Redshift tables are un-compressed because most people don’t do it.

1. list all the big tables by size, here’s a script
2. run analyze compression

   analyze compression public.report_table_name;

   Example results:

   table	column	encoding	est_reduction_pct
   report_table_name	rowid	raw	0
   report_table_name	create_date	zstd	9.49
   report_table_name	create_day	delta	38.72
   report_table_name	create_date_key	zstd	28.98
   report_table_name	type	zstd	67.59
   report_table_name	type_desc	zstd	58.25
   report_table_name	created_by	zstd	35.17

3. recreate table with new compression encoding and see performance gains.

DROP TABLE IF EXISTS public.report_table_name cascade;

CREATE TABLE public.report_table_name
( rowid INT IDENTITY(1,1),
create_date timestamp encode zstd,
create_day date encode zstd,
type integer encode zstd,
type_desc varchar(28) encode zstd,
created_by varchar(30) encode zstd,
data_refresh_date date encode raw
) ;

Amazon manual regarding compression command:

http://docs.aws.amazon.com/redshift/latest/dg/r_ANALYZE_COMPRESSION.html

Slide Share’s full of useful knowledge

Best practices for PySpark programming

Programming in Spark using PySpark from Mostafa Elzoghbi

History of SQL and all the advanced features over the last 30 years among big vendors.

Modern SQL in Open Source and Commercial Databases from Markus Winand