As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
As cloud computing continues to gather speed, organizations with years’ worth of data stored on legacy on-premise technologies are facing issues with scale, speed, and complexity. Your customers and business partners are likely eager to get data from you, especially if you can make the process easy and secure.
Challenges with performance are not uncommon and ongoing interventions are required just to “keep the lights on”.
Discover how Snowflake empowers you to meet your analytics needs by unlocking the potential of your data.
Agenda of Webinar :
~Understand Snowflake and its Architecture
~Quickly load data into Snowflake
~Leverage the latest in Snowflake’s unlimited performance and scale to make the data ready for analytics
~Deliver secure and governed access to all data – no more silos
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
Apache Iceberg Presentation for the St. Louis Big Data IDEAAdam Doyle
Presentation on Apache Iceberg for the February 2021 St. Louis Big Data IDEA. Apache Iceberg is an alternative database platform that works with Hive and Spark.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
by Darin Briskman, Technical Evangelist, AWS
Database Freedom means being able to use the database engine that’s right for you as your needs evolve. Being locked into a specific technology can prevent you from achieving your mission. Fortunately, AWS Database Migration Service makes it easy to switch between different database engines. We’ll look at how to use Schema Migration Tool with DMS to switch from a commercial database to open source. You’ll need a laptop with a Firefox or Chrome browser.
A 30 day plan to start ending your data struggle with SnowflakeSnowflake Computing
Organizations everywhere are struggling to load, integrate, analyze and collaborate with data. This is largely thanks to their antiquated data platform, designed in a time when few people had the desire or need to interact with the database. Snowflake, the data warehouse built for the cloud, can help.
Cluster computing frameworks such as Hadoop or Spark are tremendously beneficial in processing and deriving insights from data. However, long query latencies make these frameworks sub-optimal choices to power interactive applications. Organizations frequently rely on dedicated query layers, such as relational databases and key/value stores, for faster query latencies, but these technologies suffer many drawbacks for analytic use cases. In this session, we discuss using Druid for analytics and why the architecture is well suited to power analytic applications.
User-facing applications are replacing traditional reporting interfaces as the preferred means for organizations to derive value from their datasets. In order to provide an interactive user experience, user interactions with analytic applications must complete in an order of milliseconds. To meet these needs, organizations often struggle with selecting a proper serving layer. Many serving layers are selected because of their general popularity without understanding the possible architecture limitations.
Druid is an analytics data store designed for analytic (OLAP) queries on event data. It draws inspiration from Google’s Dremel, Google’s PowerDrill, and search infrastructure. Many enterprises are switching to Druid for analytics, and we will cover why the technology is a good fit for its intended use cases.
Speaker
Nishant Bangarwa, Software Engineer, Hortonworks
Every day, businesses across a wide variety of industries share data to support insights that drive efficiency and new business opportunities. However, existing methods for sharing data involve great effort on the part of data providers to share data, and involve great effort on the part of data customers to make use of that data.
However, existing approaches to data sharing (such as e-mail, FTP, EDI, and APIs) have significant overhead and friction. For one, legacy approaches such as e-mail and FTP were never intended to support the big data volumes of today. Other data sharing methods also involve enormous effort. All of these methods require not only that the data be extracted, copied, transformed, and loaded, but also that related schemas and metadata must be transported as well. This creates a burden on data providers to deconstruct and stage data sets. This burden and effort is mirrored for the data recipient, who must reconstruct the data.
As a result, companies are handicapped in their ability to fully realize the value in their data assets.
Snowflake Data Sharing allows companies to grant instant access to ready-to-use data to any number of partners or data customers without any data movement, copying, or complex pipelines.
Using Snowflake Data Sharing, companies can derive new insights and value from data much more quickly and with significantly less effort than current data sharing methods. As a result, companies now have a new approach and a powerful new tool to get the full value out of their data assets.
How to Take Advantage of an Enterprise Data Warehouse in the CloudDenodo
Watch full webinar here: [https://buff.ly/2CIOtys]
As organizations collect increasing amounts of diverse data, integrating that data for analytics becomes more difficult. Technology that scales poorly and fails to support semi-structured data fails to meet the ever-increasing demands of today’s enterprise. In short, companies everywhere can’t consolidate their data into a single location for analytics.
In this Denodo DataFest 2018 session we’ll cover:
Bypassing the mandate of a single enterprise data warehouse
Modern data sharing to easily connect different data types located in multiple repositories for deeper analytics
How cloud data warehouses can scale both storage and compute, independently and elastically, to meet variable workloads
Presentation by Harsha Kapre, Snowflake
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Data driven organizations can be challenged to deliver new and growing business intelligence requirements from existing data warehouse platforms, constrained by lack of scalability and performance. The solution for customers is a data warehouse that scales for real-time demands and uses resources in a more optimized and cost-effective manner. Join Snowflake, AWS and Ask.com to learn how Ask.com enhanced BI service levels and decreased expenses while meeting demand to collect, store and analyze over a terabyte of data per day. Snowflake Computing delivers a fast and flexible elastic data warehouse solution that reduces complexity and overhead, built on top of the elasticity, flexibility, and resiliency of AWS.
Join us to learn:
• Learn how Ask.com eliminates data redundancy, and simplifies and accelerates data load, unload, and administration
• Learn how to support new and fluid data consumption patterns with consistently high performance
• Best practices for scaling high data volume on Amazon EC2 and Amazon S3
Who should attend: CIOs, CTOs, CDOs, Directors of IT, IT Administrators, IT Architects, Data Warehouse Developers, Database Administrators, Business Analysts and Data Architects
Apache Iceberg Presentation for the St. Louis Big Data IDEAAdam Doyle
Presentation on Apache Iceberg for the February 2021 St. Louis Big Data IDEA. Apache Iceberg is an alternative database platform that works with Hive and Spark.
Snowflake concepts & hands on expertise to help get you started on implementing Data warehouses using Snowflake. Necessary information and skills that will help you master Snowflake essentials.
Organizations are struggling to make sense of their data within antiquated data platforms. Snowflake, the data warehouse built for the cloud, can help.
by Darin Briskman, Technical Evangelist, AWS
Database Freedom means being able to use the database engine that’s right for you as your needs evolve. Being locked into a specific technology can prevent you from achieving your mission. Fortunately, AWS Database Migration Service makes it easy to switch between different database engines. We’ll look at how to use Schema Migration Tool with DMS to switch from a commercial database to open source. You’ll need a laptop with a Firefox or Chrome browser.
A 30 day plan to start ending your data struggle with SnowflakeSnowflake Computing
Organizations everywhere are struggling to load, integrate, analyze and collaborate with data. This is largely thanks to their antiquated data platform, designed in a time when few people had the desire or need to interact with the database. Snowflake, the data warehouse built for the cloud, can help.
Cluster computing frameworks such as Hadoop or Spark are tremendously beneficial in processing and deriving insights from data. However, long query latencies make these frameworks sub-optimal choices to power interactive applications. Organizations frequently rely on dedicated query layers, such as relational databases and key/value stores, for faster query latencies, but these technologies suffer many drawbacks for analytic use cases. In this session, we discuss using Druid for analytics and why the architecture is well suited to power analytic applications.
User-facing applications are replacing traditional reporting interfaces as the preferred means for organizations to derive value from their datasets. In order to provide an interactive user experience, user interactions with analytic applications must complete in an order of milliseconds. To meet these needs, organizations often struggle with selecting a proper serving layer. Many serving layers are selected because of their general popularity without understanding the possible architecture limitations.
Druid is an analytics data store designed for analytic (OLAP) queries on event data. It draws inspiration from Google’s Dremel, Google’s PowerDrill, and search infrastructure. Many enterprises are switching to Druid for analytics, and we will cover why the technology is a good fit for its intended use cases.
Speaker
Nishant Bangarwa, Software Engineer, Hortonworks
Every day, businesses across a wide variety of industries share data to support insights that drive efficiency and new business opportunities. However, existing methods for sharing data involve great effort on the part of data providers to share data, and involve great effort on the part of data customers to make use of that data.
However, existing approaches to data sharing (such as e-mail, FTP, EDI, and APIs) have significant overhead and friction. For one, legacy approaches such as e-mail and FTP were never intended to support the big data volumes of today. Other data sharing methods also involve enormous effort. All of these methods require not only that the data be extracted, copied, transformed, and loaded, but also that related schemas and metadata must be transported as well. This creates a burden on data providers to deconstruct and stage data sets. This burden and effort is mirrored for the data recipient, who must reconstruct the data.
As a result, companies are handicapped in their ability to fully realize the value in their data assets.
Snowflake Data Sharing allows companies to grant instant access to ready-to-use data to any number of partners or data customers without any data movement, copying, or complex pipelines.
Using Snowflake Data Sharing, companies can derive new insights and value from data much more quickly and with significantly less effort than current data sharing methods. As a result, companies now have a new approach and a powerful new tool to get the full value out of their data assets.
How to Take Advantage of an Enterprise Data Warehouse in the CloudDenodo
Watch full webinar here: [https://buff.ly/2CIOtys]
As organizations collect increasing amounts of diverse data, integrating that data for analytics becomes more difficult. Technology that scales poorly and fails to support semi-structured data fails to meet the ever-increasing demands of today’s enterprise. In short, companies everywhere can’t consolidate their data into a single location for analytics.
In this Denodo DataFest 2018 session we’ll cover:
Bypassing the mandate of a single enterprise data warehouse
Modern data sharing to easily connect different data types located in multiple repositories for deeper analytics
How cloud data warehouses can scale both storage and compute, independently and elastically, to meet variable workloads
Presentation by Harsha Kapre, Snowflake
Building Reliable Lakehouses with Apache Flink and Delta LakeFlink Forward
Flink Forward San Francisco 2022.
Apache Flink and Delta Lake together allow you to build the foundation for your data lakehouses by ensuring the reliability of your concurrent streams from processing to the underlying cloud object-store. Together, the Flink/Delta Connector enables you to store data in Delta tables such that you harness Delta’s reliability by providing ACID transactions and scalability while maintaining Flink’s end-to-end exactly-once processing. This ensures that the data from Flink is written to Delta Tables in an idempotent manner such that even if the Flink pipeline is restarted from its checkpoint information, the pipeline will guarantee no data is lost or duplicated thus preserving the exactly-once semantics of Flink.
by
Scott Sandre & Denny Lee
Building Data Quality pipelines with Apache Spark and Delta LakeDatabricks
Technical Leads and Databricks Champions Darren Fuller & Sandy May will give a fast paced view of how they have productionised Data Quality Pipelines across multiple enterprise customers. Their vision to empower business decisions on data remediation actions and self healing of Data Pipelines led them to build a library of Data Quality rule templates and accompanying reporting Data Model and PowerBI reports.
With the drive for more and more intelligence driven from the Lake and less from the Warehouse, also known as the Lakehouse pattern, Data Quality at the Lake layer becomes pivotal. Tools like Delta Lake become building blocks for Data Quality with Schema protection and simple column checking, however, for larger customers they often do not go far enough. Notebooks will be shown in quick fire demos how Spark can be leverage at point of Staging or Curation to apply rules over data.
Expect to see simple rules such as Net sales = Gross sales + Tax, or values existing with in a list. As well as complex rules such as validation of statistical distributions and complex pattern matching. Ending with a quick view into future work in the realm of Data Compliance for PII data with generations of rules using regex patterns and Machine Learning rules based on transfer learning.
Manual de inspeccion de equipos de aplicacion de fitosanitariosGuadalinfo Escañuela
Manual para la inspección de equipos de aplicación fitosanitaria.
La presente monografía se ha realizado dentro del “Programa para la formación, divulgación y difusión del plan de calibración de equipos de aplicación de fitosanitarios, (2013-2014)” establecido en función de un contrato de servicios mediante procedimiento negociado entre la Consejería de Agricultura, Pesca y Desarrollo Rural y la Universidad de Córdoba.
Coordinadores: Antonio Rodríguez Ocaña.
Mª. del Carmen Castro Mora
Autores: Gregorio L. Blanco Roldán
Jesús A. Gil Ribes
Juan Luis Gamarra Diezma
Alfonso José Guillén Dana
Antonio Miranda Fuentes
Edita: Junta de Andalucía.
Consejería de Agricultura, Pesca y Desarrollo Rural.
Publica: Servicio de Publicaciones y Divulgación.
Producción editorial:
Serie: Agricultura. Guías prácticas.
Hi,I have implemented increment() method. Please find the below up.pdfAnkitchhabra28
Hi,
I have implemented increment() method. Please find the below updated code.
DaysBetween Class:
import java.text.DateFormatSymbols;
import java.util.Calendar;
import java.util.Scanner;
class DateClass {
protected int year;
protected int month;
protected int day;
public static final int MINYEAR = 1583;
// Constructor
public DateClass(int newMonth, int newDay, int newYear)
{
month = newMonth;
day = newDay;
year = newYear;
}
// Observers
public int getYear()
{
return year;
}
public int getMonth()
{
return month;
}
public int getDay()
{
return day;
}
public int lilian()
{
// Returns the Lilian Day Number of this date.
// Precondition: This Date is a valid date after 10/14/1582.
//
// Computes the number of days between 1/1/0 and this date as if no calendar
// reforms took place, then subtracts 578,100 so that October 15, 1582 is day 1.
final int subDays = 578100; // number of calculated days from 1/1/0 to 10/14/1582 November
17, 1858
int numDays;
// Add days in years.
numDays = year * 365;
// Add days in the months.
if (month <= 2)
numDays = numDays + (month - 1) * 31;
else
numDays = numDays + ((month - 1) * 31) - ((4 * (month-1) + 27) / 10);
// Add days in the days.
numDays = numDays + day;
// Take care of leap years.
numDays = numDays + (year / 4) - (year / 100) + (year / 400);
// Handle special case of leap year but not yet leap day.
if (month < 3)
{
if ((year % 4) == 0) numDays = numDays - 1;
if ((year % 100) == 0) numDays = numDays + 1;
if ((year % 400) == 0) numDays = numDays - 1;
}
// Subtract extra days up to 10/14/1582.
numDays = numDays - subDays;
return numDays;
}
Override
public String toString()
// Returns this date as a String.
{
String monthString = new DateFormatSymbols().getMonths()[month-1];
return(monthString + \"/\" + day + \"/\" + year);
}
public class mjd
{
public int mjd()
{
final int subDays = 678941;
int numDays;
numDays = year * 365;
if (month <= 2)
numDays = numDays + (month - 1) * 31;
else
numDays = numDays + ((month -1) * 31) - ((4 * (month-1) + 27)/10);
numDays = numDays + day;
numDays = numDays + (year / 4) - (year / 100) + (year / 400);
if (month < 3)
{
if ((year % 4) == 0) numDays = numDays -1;
if ((year % 100) == 0) numDays = numDays + 1;
if ((year % 400) == 0) numDays -= numDays -1;
}
// Days subtracted up to 10/14/1582
numDays = numDays - subDays;
return numDays;
}
}
public class djd
{
public int djd()
{
final int subDays = 693961; // number of calculated days from 1/1/0 to January 1,1900
int numDays;
// Add days in years.
numDays = year * 365;
// Add days in the months.
if (month <= 2)
numDays = numDays + (month - 1) * 31;
else
numDays = numDays + ((month - 1) * 31) - ((4 * (month-1) + 27) / 10);
// Add days in the days.
numDays = numDays + day;
// Take care of leap years.
numDays = numDays + (year / 4) - (year / 100) + (year / 400);
// Handle special case of leap year but not yet leap day.
if (month < 3)
{
if ((year % 4) == 0) numDays = numDays - 1;
if ((year % 100) == 0) numDays = numDays + 1;
if ((year % .
This presentation provides an overview of using the Java SE 8 Date & Time API. It covers how to:
1. Create and manage date-based and time-based events including a combination of date and time into a single object using LocalDate, LocalTime, LocalDateTime, Instant, Period, and Duration
2. Work with dates and times across timezones and manage changes resulting from daylight savings including format date and times values
3. Define and create and manage date-based and time-based events using Instant, Period, Duration, and TemporalUnit
Libraries and History
The “old” Date/Calendar classes
The new (≥Java8) java.time package
Basic concepts
Main classes
Date operations
Dealing with SQL dates
Teaching material for the course of "Tecniche di Programmazione" at Politecnico di Torino in year 2014/2015. More information: http://bit.ly/tecn-progr
Assignment Details There is a .h file on Moodle that provides a defi.pdfjyothimuppasani1
Assignment Details There is a .h file on Moodle that provides a definition for a
WeatherForecaster class. The functionality for that class is similar to the functionality you
implemented in Assignment 5, with a few additional functions. Instead of using an array of
structs and functions to process the array, you will create one WeatherForecaster object that
includes the array of structs as a private variable and public methods to process the data. The
struct for this assignment has an additional member called forecastDay, you will need to store all
of the data this time. struct ForecastDay{ string day; string forecastDay; int highTemp; int
lowTemp; int humidity; int avgWind; string avgWindDir; int maxWind; string maxWindDir;
double precip; }; Methods in the WeatherForecaster class void addDayToData(ForecastDay); •
Takes a ForecastDay as an argument and adds it to the private array stored in the
WeatherForecaster object. • Use the private index variable to control where ForecastDay is
added to the array. void printDaysInData(); • Show the dates in the data set where the day and
the forecast day are the same. void printForecastForDay(string); • Take a date as an argument
and shows the forecast for that date. CSCI 1310 - Assignment 6 Due Saturday, Oct 15, by 12:30
pm void printFourDayForecast(string); • Takes a date as an argument and shows the forecast
issued on that date and for the next three days. For example, for a date of 1- 26-2016, you would
show the forecast for 1-26-2016 issued on 1- 26-2016 as well as the forecast for 1-27, 1-28, and
1-29 issued on 1-26. double calculateTotalPrecipitation(); • Returns the sum of the precipitation
in the data set. void printLastDayItRained(); • Shows the date of the last measureable
precipitation. void printLastDayAboveTemperature(int); • Takes an integer as an argument and
shows the date for the last day above that temperature. If no days are above the temperature,
prints “No days above that temperature.” void printTemperatureForecastDifference(string); •
Takes a date as an argument and shows the temperature forecast for that date for the three days
leading up to the date and the day-of forecast. void printPredictedVsActualRainfall(int); • Shows
the difference between the predicted and actual rainfall total in the entire data set. • The
argument to the function is the number of forecast days away. For example, the forecast for 1-27-
2016 is one day away from 1- 26-2016. string getFirstDayInData(); • Returns the first date in the
data with a day-of forecast, i.e. day = forecastDay string getLastDayInData(); • Returns the last
date in the data with a day-of forecast, i.e. day = forecastDay Challenge functions 1. There is
another header file on Moodle called WeatherForecastChallenge.h that uses a vector to store the
future forecast days. Instead of including all data in the yearData array, you can include only
days where the day = forecast day in the array. The other forecast days are stored in the vecto.
Similar to Date and Timestamp Types In Snowflake (By Faysal Shaarani) (20)
Assignment Details There is a .h file on Moodle that provides a defi.pdf
Date and Timestamp Types In Snowflake (By Faysal Shaarani)
1. Taming The Snowflake DATE & TIMESTAMP Data
Manipulation & Arithmetic
(Faysal Shaarani)
Date and Time calculations are among the most widely used and most critical
computations in Analytics and Data Mining. The objective of this document is to
make your experience with Dates and Timestamps in Snowflake a smooth and
simple one.
Snowflake supports DATE and TIMESTAMP data types:
1. DATE: The DATE type stores dates (without time). Accepts dates in the
most common forms such as YYYY-MM-DD or DD-MON-YYYY etc. All
accepted timestamps are valid inputs for dates as well.
2. TIMESTAMP: Snowflake supports three flavors of the TIMESTAMP type
and a special TIMESTAMP alias. TIMESTAMP type in Snowflake is a
user-defined alias to one of the three types. In all operations where
TIMESTAMP can be used, the specified TIMESTAMP_ flavor will be used
automatically. The actual target type is controlled by
the TIMESTAMP_TYPE_MAPPING configuration option (by default it
is TIMESTAMP_LTZ) and TIMESTAMP type is never stored in the tables.
The timestamp_type_mapping can be set via the following command:
ALTER SESSION SET timestamp_type_
mapping = default;
The three TIMESTAMP types are:
a. TIMESTAMP_LTZ type internally stores UTC time with a specified
precision. All operations are performed in the current session's time
zone, controlled by the TIMEZONE parameter and can be changed
via the ALTER SESSION command:
ALTER SESSION SET
timezone = ‘America/Los_Angeles’;
2. b. TIMESTAMP_NTZ type internally stores "wallclock" time with a
specified precision. All operations are performed without taking any
time zone into account.
c. TIMESTAMP_TZ type internally stores UTC time together with an
associated time zone. When not provided, the session time zone is
used. All operations are performed in the time zone specific for
each record.
The DATE data type in Snowflake contains only date values (without the time
component). The TIMESTAMP data types in Snowflake contain date and time,
and optionally timezone.
Calendar Weeks and Weekdays
In Snowflake, the calendar week starts on Monday, following the ISO-8601
standard. This behavior influences functions like DATEDIFF and DATE_TRUNC.
Also, when extracting the “week” component in functions like DATE_PART and
EXTRACT, ISO week number is returned.
The “dayofweek_iso” component for EXTRACT and DATE_PART follows the
ISO behavior, returning 1 for Monday, 2 for Tuesday, …, 6 for Saturday and 7 for
Sunday. For compatibility with some other systems, the “dayofweek”
component returns 1 for Monday, 2 for Tuesday, …, 6 for Saturday, and 0 for
Sunday (following standard UNIX nomenclature).
Getting Current Date/Time
To get [Today’s Date] as DATE:
select current_date();
2014-08-05
To get [Today’s Date & Time] as TIMESTAMP_LTZ:
select current_timestamp();
Mon, 04 Aug 2014 17:13:02 -0700
Extracting values
To get the [Day of the Week] as number (can be applied to any date or
timestamp):
select extract('dayofweek',current_date())
3. To get the [Name of the Day of the Week] as text (using CURRENT_DATE() as
an example):
This produces short English names, e.g. ‘Sun’, ‘Mon’ etc.
select to_varchar(current_date(), 'DY');
To use arbitrary, explicitly provided weekday names:
select DECODE(
extract ('dayofweek_iso',current_date()),
1, 'Monday',
2, 'Tuesday',
3, 'Wednesday',
4, 'Thursday',
5, 'Friday',
6, 'Saturday',
7, 'Sunday')
Computing Business Calendar:
To get the [First Day of the Current Month ] as DATE
SELECT DATE_TRUNC('month', current_date());
To get the [Last Day of the Current Month] as DATE:
select dateadd('day', -1,
dateadd('month', 1,
date_trunc('month', current_date())));
NOTE: In the above example, date_trunc finds the beginning of the current
month, the following addition of 1 month finds the beginning of the next month,
and final subtraction of 1 day finds the last day in the current month.
To get the [Last Day of the Prior Month] as DATE:
select dateadd(day, -1,
date_trunc('month',current_date()) );
To get the [Month of the Year By Name]:
Simple mode, using English abbreviated month names, e.g. “Jan” and “Dec”
select to_varchar(current_date(), 'Mon');
Using arbitrary, explicitly provided month names:
4. select DECODE extract('month',current_date())
1 , 'January',
2 , 'February',
3 , 'March',
4 , 'April',
5 , 'May',
6 , 'June',
7 , 'July',
8 , 'August',
9 , 'September',
10, 'October',
11, 'November',
12, 'December');
To get the [Date of the Monday of the Current Week]:
select dateadd(day, (extract('dayofweek_iso',
current_date()) * -1) +1 , current_date() );
To get the [Date of the Friday of the Current Week]:
Select dateadd('day', (5 - extract('dayofweek_iso',
current_date()) ), current_date() );
To get the [First Day of the Current Year] as DATE:
select date_trunc('year', current_date());
To get the [First Monday of the Current Month]:
select dateadd(
day,
MOD( 7 + 1 - date_part('dayofweek_iso',
date_trunc('month', current_date()) ), 7),
date_trunc('month', current_date()));
Note: “1” in the “7+1” above results in Monday. Use 2 for Tuesday…7 for
Sunday etc.
To get the [Last Day of the Current Year] as DATE:
select dateadd('day', -1,
dateadd('year', 1,
date_trunc('year', current_date())));
Note: To get the last day of the current month, use "month" instead of "year" on
the above SQL.
To get the [Last Day of the Prior Year] as DATE:
select dateadd('day', -1,
date_trunc('year',current_date()) );
5. To get the [First Day of the Quarter] as DATE:
select date_trunc('quarter',current_date());
To get the [Last Day of the Quarter] as DATE:
select dateadd('day', -1,
dateadd('month', 3,
date_trunc('quarter', current_date())));
To get [Midnight Time (Start of the day) of the Current Day]:
select date_trunc('day', current_timestamp() );
Other Date and Timestamp Operations
To get the [Date or Time Part of today’s Date and Time]:
select date_part(day, current_timestamp());
select date_part(year, current_timestamp());
select date_part(month, current_timestamp());
select date_part(hour, current_timestamp());
select date_part(minute, current_timestamp());
select date_part(second, current_timestamp());
OR
select extract('day', current_timestamp());
select extract('year', current_timestamp());
select extract('month', current_timestamp());
select extract('hour', current_timestamp());
select extract('minute', current_timestamp());
select extract('second', current_timestamp());
OR
select day(current_timestamp() ) ,
hour( current_timestamp() ),
second(current_timestamp()),
minute(current_timestamp()),
month(current_timestamp());
NOTE: Please refer to the table below for additional Date/Time Parts
Masks.
[Date/Time Parts Masks]:
The following table lists different parts of dates and times that can be used by
various functions.
6. Date part or time part Abbreviations
Supported by
functions
Notes
year, years
y, yr, yrs, yy, yyy,
yyyy
extract, date_part,
trunc, date_trunc,
dateadd, datediff
quarter, quarters q, qtr, qtrs trunc, date_trunc
month, months mm, mon, mons
extract, date_part,
trunc, date_trunc,
dateadd, datediff
day, days d, dd
extract, date_part,
trunc, date_trunc,
dateadd, datediff
dayofweek, weekday dow, dw extract, date_part
Values returned are from 0
(Sunday) to 6 (Saturday). Note that
"week" component returns weeks
starting on Monday.
dayofweek_iso,
weekday_iso
dow_iso, dw_iso extract, date_part
Values returned are from 1
(Monday) to 7 (Sunday).
dayofyear, yearday doy, dy extract, date_part
week w, wk
extract, date_part,
date_trunc,
dateadd, datediff,
ISO week (starting on Monday). In
EXTRACT/DATE_PART, the
returned week number
corresponds to ISO 8601 weeks,
where a week belongs to the year
that contains a Thursday of that
week. It means, that the value
7. returned for days in early January
can be 52 or 53 (week belonging to
the previous year), and for days in
late December can be 1 (week
belonging to the next year).
weekofyear, wy, woy extract, date_part See discussion for "week".
hour, hours h, hr, hrs, hh
extract, date_part,
trunc, date_trunc,
dateadd, datediff
minute, minutes m, mi, min, mins
extract, date_part,
trunc, date_trunc,
dateadd, datediff
second, seconds s, sec, secs
extract, date_part,
trunc, date_trunc,
dateadd, datediff
nanosecond,
nanoseconds
ns, nsec, nsecs,
nsecond, nseconds,
nanosec, nanosecs
extract, date_part
timezone_hour tzh extract, date_part
timezone_minute tzm extract, date_part
To add [Different Time Increments to a Date Value]:
select dateadd(year, 2, current_date());
select dateadd(day,2,current_date());
select dateadd(hour,2,current_timestamp());
select dateadd(minute,2,current_timestamp());
select dateadd(second,2,current_timestamp());
8. To [Convert a Valid Character String to a Timestamp]:
select to_timestamp ('12-jan-2013 00:00:00','dd-mon-yyyy
hh:mi:ss');
To [Perform Date Arithmetic on a Valid Date String]:
select dateadd('day',5, to_timestamp ('12-jan-2013
00:00:00','dd-mon-yyyy hh:mi:ss') );
select datediff('day', to_timestamp ('12-jan-2013
00:00:00','dd-mon-yyyy hh:mi:ss') , current_date() );
select datediff('day', to_date ('12-jan-2013 00:00:00','dd-
mon-yyyy hh:mi:ss') , current_date() );
To [Insert a Valid Date String Into a Table with Date Column]:
Create table test (date1 date);
insert into test values (to_date ('12-jan-2013
00:00:00','dd-mon-yyyy hh:mi:ss'));
2013-01-12
insert into test values (to_date ('11:30:40','hh:mi:ss'));
1970-01-01
select to_varchar(date1, 'dd-mon-yyyy hh:mi:ss') from test;
12-Jan-2013 00:00:00
01-Jan-1970 00:00:00
To compute [The Difference Between Two Dates]:
select datediff(year, current_date(),
dateadd(year, 3, current_date() ) );
select datediff(month, current_date(),
dateadd(month, 3, current_date()) );
select datediff(day, current_date(),
dateadd(day, 3, current_date()) );
select datediff(hour, current_timestamp(),
dateadd(hour, 3, current_timestamp()) );
select datediff(minute, current_timestamp(),
dateadd(minute, 3, current_timestamp()) );
select datediff(second, current_timestamp(),
dateadd(second, 3, current_timestamp()) );
9. To create a [Yearly Calendar View in Snowflake]:
create or replace view calendar_2014 as
select n, theDate,
decode (extract('dayofweek',theDate),
1 , 'Monday',
2 , 'Tuesday',
3 , 'Wednesday',
4 , 'Thursday',
5 , 'Friday',
6 , 'Saturday',
0 , 'Sunday'
) theDayOfTheWeek,
decode (extract(month from theDate),
1 , 'January',
2 , 'February',
3 , 'March',
4 , 'April',
5 , 'May',
6 , 'June',
7 , 'July',
8 , 'August',
9 , 'september',
10, 'October',
11, 'November',
12, 'December'
) theMonth,
extract(year from theDate) theYear
from
( select
row_number() over (order by seq4()) as n,
dateadd(day, row_number() over (order by
seq4())-1, to_date('2014-01-01'))
as theDate
from table(generator(rowCount => 365))) order
by n asc;