New Year, New Site

I switched to wordpress.com as my host. I will most likely switch to AWS later.

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Amazon Redshift’s Unsupported Features of PostGres

Redshift is based off branch of PostGreSQL 8.0.2 [ PostgreSQL 8.0.2 was released in 2005]

here’s all the unsupported fancy PostGres Stuff: taken directly from amazon’s manual.

The bigs ones are: No Store Procedures, No Constraints enforcement, No triggers and no table functions, no upserts.

However, don’t ever forget that for an Amazon Redshift query can do:

Select count(disinct column_name) from table.

200x faster than PostGres on a Billion row table.

Unsupported PostgreSQL Features

These PostgreSQL features are not supported in Amazon Redshift.

Important

Do not assume that the semantics of elements that Amazon Redshift and PostgreSQL have in common are identical. Make sure to consult the Amazon Redshift Developer Guide SQL Commands to understand the often subtle differences.

  • Only the 8.x version of the PostgreSQL query tool psql is supported.
  • Table partitioning (range and list partitioning)
  • Tablespaces
  • Constraints
    • Unique
    • Foreign key
    • Primary key
    • Check constraints
    • Exclusion constraints

    Unique, primary key, and foreign key constraints are permitted, but they are informational only. They are not enforced by the system, but they are used by the query planner.

  • Inheritance
  • Postgres system columnsAmazon Redshift SQL does not implicitly define system columns. However, the PostgreSQL system column names cannot be used as names of user-defined columns. See http://www.postgresql.org/docs/8.0/static/ddl-system-columns.html
  • Indexes
  • NULLS clause in Window functions
  • CollationsAmazon Redshift does not support locale-specific or user-defined collation sequences. See Collation Sequences.
  • Value expressions
    • Subscripted expressions
    • Array constructors
    • Row constructors
  • Stored procedures
  • Triggers
  • Management of External Data (SQL/MED)
  • Table functions
  • VALUES list used as constant tables
  • Recursive common table expressions
  • Sequences
  • Full text search
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Best Practices for Micro-Batch Loading on Amazon Redshift

Best Practices for Micro-Batch Loading on Amazon Redshift Article by AWS blog

I work with Redshift everyday now at Amazon. It’s very useful big data warehouse tool.
Here’s a blog post about loading data into it. It’s very s3 dependent and heavy use of the Copy command.

Some quick notes:
-It’s faster to drop and load big tables into staging areas.
-Split input files in to pieces and load in parallel.
-COPY option ‘STATUPDATE OFF.’
-Avoid Vacuum of tables when possible

You could just read the main points in the how to guide.

here’s quick and eas do the following in a single transaction:
1. Create staging table “tablename_staging” like main table
2. Copy data from S3 into staging table
3. Delete rows in main table that are already present in staging table
4. Copy all rows from staging table to main table
5. Drop staging table

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Amazon Redshift is an amazing database product

Redshift is :
Fast like Ferrari
Cheap like a Ford Fiesta
Useful like a Minivan
Self Driving Auto-magics like Tesla with Autopilot

Key features:

Really fancy features under-the-hood:
-interleaved sort keys
-columnar distributed storage
-smart parallel execution
-IO optimization (return results fast)
-Easy to add nodes and scalable
-Shared nothing architecture
-Less need for dbas, monitor and use in AWS console

Caveats:
-Amazon Cloud only
-Requirements of S3 dependability
-Only useful for very very large datasets
-A limited number concurrent queries

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Review of two New Cloud BI tools : Snowflake and Looker

Snowflake: data warehouse in the cloud (specially amazon)

Snowflake compute is basically an analytics computing database that has scalability. Data is stored / shared on AWS S3 buckets instead of in snowfalek. You spin up snowflake tool injest and load into it’s proprietary parallel columnar sql engine for analytics processing. Afterwards you run sql against it. It’s mostly PL/SQL and/or TSQL like syntax. In the world of hadoop snowflake is  sql based data warehouse you run on demand.

Pros:

  • Magically scale up/down compute nodes  with a few clicks. No need for database tuning.
  • Ingest JSON data directly from flatfiles/tez files.
  • Pay only for what you use platform.
  • Sql based platform so no need to learn another language.

Cons:

  • Amazon Cloud locked  Vendor
  • All your data need to  be in S3 and cleaned into a semi-structured manner to read and load into snowflake.
  • SQL is not completely up-to-date with latest (missing window functions etc.)

Looker: Cloud based Business Intelligence Reporting and Dashboards. 

Looker’s a new BI tool. It does reports, dashboards, and allow data exploration. The thing that makes it different is it cloud hosted, it uses more developer like frameworks (LookML like yaml language syntax, GIT version control, releases pushes).  It’s very fast to build up in the hands of a seasoned data engineer / data architect. It also simplifies a lot of common data warehouse tasks (auto generate time dimension lookups, rolling totals, data manipulation), and it has connectors to most data sources via jdbc.

Pros:

  • Easy to setup quickly and get baseline reports working.
  • Cloud based so you can just point to your db and host on them or setup on premise instance yourself.
  • GIT version control and rollback, something not in most BI tools.
  • Relatively cheap to embed into existing applications.

Cons:

  • Still quite new and doesn’t have all mature BI  features built out.
  • Visualizations still simple grids, bars, lines, pies and etc.
  • Requires  a fairly technical person to setup lookML schemas before business analysts can self service and data explore.
  • Need to know sql well to troubleshoot results.

Common between Looker and Snowflake is that they’re both AWS Cloud based, easy to get setup fast, and use SQL as the lingua franca.

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DevFestDC : key takeaways of the Google Cloud Products

TensorFLow : google’s machine learning api can be super powerful.
Just make json REST calls to the end-point and get results based on google’s machine learning lib.
Uses cases:
1. identify an image (image classification)
2. parse speech into text
3. translate languages
4. use for predictive analytics to run wide and deep learning on datasets

Google Cloud Platform : It is Google’s AWS cloud.
Key takeaways: They only charge by the minute and not by the hour like amazon.
Extremely cheap pay as you use model on an extremely fast network; Access to Google’s pipes and servers.

A ton of of useful Cloud tools: SaaS, PaaS, BaaS etc..

Compute Engine : AWS EC2 boxes except more scaleable.
App Engine : AWS Beanstalk
Cloud Storage : AWS S3

data tools : the ones I’m interested in.

Google’s Big Table : Similar to amazon DynoDB, a nosql data store. The basic store engine of gmail,googlemaps and etc. Google’s original Hbase hapdoop data store.

Google BigQuery : Web interface to scan and query millions, billions, trillions of rows.

Google DataProc : Managed Hadoop, Spark Pig, Hive combined into one interface. Spin up a multiple  cluster nodes in seconds and run a spark, pig, hive job using google’s compute power.
(better than amazon emr)

Summary: pay-as-you-go for cloud computing service. Watch out Amazon Web Services and Azure.

google data studio : neat visualization app for data.

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basic database table creation and load from csv using mysql and postgres

Basic database table creation with MySql and PostGreSQL.
The starting point to most data applications is getting the data feeds and populating the tables.
here’s an example of the process I’m loading a stock_history table from yahoo finance api source.

Make a table :

mysql:

create table stock_history
( id bigint not null primary key AUTO_INCREMENT,
symbol varchar(10) not null,
data_date date not null,
open_at decimal(14,6) null,
close_at decimal(14,6) null,
high decimal(14,6) null,
low decimal(14,6) null,
volume decimal(24,0) null
) ;
create index sidx on stock_history (symbol);

postgres:

create table stock_history
( id SERIAL primary key,
symbol varchar not null,
data_date date not null,
open_at money null,
close_at money null,
high money null,
low money null,
volume numeric null
) ;
create index sidx on stock_history (symbol);

 

Load a table from CSV source
mysql:

LOAD DATA INFILE “/tmp/data_source.csv”
INTO TABLE stock_tickers
COLUMNS TERMINATED BY ‘,’
OPTIONALLY ENCLOSED BY ‘”‘
ESCAPED BY ‘”‘
LINES TERMINATED BY ‘n’
IGNORE 1 LINES;


postgres:

copy stock_history (symbol, data_date, open_at, close_at, high, low, volume)
from '/tmp/data_source.csv' WITH HEADER DELIMITER ',' csv;

I’m using stock_quote gem to source my stock csv feed.

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