Data Warehousing and Database concepts- The basics
The basic concept of a Data Warehouse is to facilitate a single version of truth for a company for decision making and forecasting. A Data warehouse is an information system that contains historical and commutative data from single or multiple sources. Data Warehouse Concepts simplify the reporting and analysis process of organizations.
- Cloud Data Warehouse vs Traditional Data Warehouse Concepts
- Cloud Data Warehouse Concepts
- Conclusion: Traditional vs. Data Warehouse Concepts in Brief
- Data Warehouse Architecture: Traditional vs. Cloud
- New Data Warehouse Architectures
- Data Mart vs. Data Warehouse
- The Difference Between a Data Warehouse and a Database
- Data Lake vs. Data Warehouse
- 12 Datawarehouse cloud tools
- Data Warehouse Testing
Modern Data Warehouse
A modern data warehouse lets you bring together all your data at any scale easily, and to get insights through analytical dashboards, operational reports, or advanced analytics for all your users.
- BI and Data Warehousing
- Data Warehousing and Data Mining
- The New EDW: Meet the Big Data Stack
- Build An ETL Process with Examples
- ETL – load data into AWS Redshift
- Full vs Incremental Loading in ETL
- Best Cloud-Based and Open Source Tools
- Get Started with ETL
- ETL Testing
- Redshift Architecture, Pricing, and Performance
- Redshift Columnar Storage
- Redshift Cluster 101
- Snowflake and the Future of Data Warehousing
- Google BigQuery Architecture
Cloud Basics and Why choose the Cloud Warehouse
Due to their architecture, cloud-based data warehouse offers some major advantages over the traditional systems, such as: Scalability; Reliability; Security; Adaptability; Many organizations cite a lack of resources and expertise as barriers to implementing an on-site data warehouse solution. This is where cloud data warehouses become a preference.
- What is cloud computing?
- Cloud Computing Service Models – IaaS, PaaS, SaaS
- Cloud Computing Deployment Models – Public, Private & Hybrid
- Cloud Computing Basics – Compute
- Cloud Computing Basics – Storage
- Cloud Computing Basics – Network
- Cloud Computing Basics – Serverless
- What is a cloud datawarehouse?
Introduction To Snowflake
Introduction to the powerful and unique features that Snowflake provides for ensuring your data is protected, secure, and available.
Connecting To Snowflake
This guide will walk you through how to connect to Snowflake. You may need to contact the administrator of your Snowflake account if you lack some of the credentials discussed below.
Loading / Unloading Data Into/From Snowflake
These topics describe the concepts and tasks for Data Loading /unloading (i.e. exporting) data from Snowflake tables. Key concepts related to data unloading, as well as best practices.
- Overview of Data Loading/Unloading
- Data Loading/Unloading Considerations
- Preparing to Load/Unload Data
- Bulk Loading/Unloading from Amazon S3
- Bulk Loading/Unloading from a Local File System
- Bulk Loading /Unloading from Microsoft Azure
- Loading Continuously Using Snowpipe
- Loading Using the Web Interface (Limited)
- Querying Data in Staged Files
- Querying Metadata for Staged Files
- Transforming Data During a Load
Database Objects and Querying
A database object is any defined object in a database that is used to store or reference data. Anything which we make from create command is known as Database Object. Some of the examples of database objects are : view, table, sequence, indexes, etc.
Sharing Data In Snowflake
Snowflake data providers can share data that resides in different databases by using secure views. A secure view can reference objects such as schemas, tables, and other views from one or more databases, as long as these databases belong to the same account.
Managing Your Snowflake Account
These topics describe the administrative concepts and tasks associated with managing your account in Snowflake. These topics are intended primarily for administrators (i.e. users with the ACCOUNTADMIN, SYSADMIN, or SECURITYADMIN roles). Account Identifier.
Welcome to Advanced Topics. The goal of this course is to provide you with a deeper understanding of optimizing the usage of snowflake.
- Informatica Cloud Services and Snowflake Integration
- Informatica Snowflake Connector
- Informatica Snowflake JDBC Connection
- Informatica Snowflake Key Range Partitioning
- Informatica Snowflake Objects in Mappings
- Informatica Snowflake Sources in Mappings
- Informatica Snowflake Targets in Mappings
- Informatica Snowflake Lookups in Mappings
- Data Clustering
- Clustering Keys & Clustered Tables
- Using the Spark Snowflake Connector
BI and Data Warehousing
For a long time, Business Intelligence and Data Warehousing were almost synonymous. You couldn’t do one without the other: for timely analysis of massive historical data, you had to organize, aggregate and summarize it in a specific format within a data warehouse.
But this dependency of BI on data warehouse infrastructure had a huge downside. Historically, data warehouses were or can be an expensive, scarce resource. They take months and millions of dollars to setup, and even when in place, they allow only very specific types of analysis. If you need to ask new questions or process new types of data, you are faced with major development efforts.
We’ll define business intelligence and data warehousing in a modern context, and raise the question of the importance of data warehouses in BI.
We offer two alternatives to a traditional BI/data warehouse paradigm:
- Instant BI in a data lake using an Extract-Load-Transform (ELT) strategy
- Automated data warehouses that allow faster time to analysis without formal ETL
What is Business Intelligence and Analytics?
Business intelligence (BI) is a process for analyzing data and deriving insights to help businesses make decisions. In an effective BI process, analysts and data scientists discover meaningful hypotheses and can answer them using available data.
For example, if management is asking “how do we improve conversion rate on the website?” BI can identify a possible cause for low conversion. The cause might be lack of engagement with website content. Within the BI system, analysts can demonstrate if engagement really is hurting conversion, and which content is the root cause.
The tools and technologies that make BI possible take data—stored in files, databases, data warehouses, or even on massive data lakes—and run queries against that data, typically in SQL format. Using the query results, they create reports, dashboards and visualizations to help extract insights from that data. Insights are used by executives, mid-management, and also employees in day-to-day operations for data-driven decisions.
What is a Data Warehouse?
A data warehouse is a relational database that aggregates structured data from across an entire organization. It pulls together data from multiple sources—much of it is typically online transaction processing (OLTP) data. The data warehouse selects, organizes and aggregates data for efficient comparison and analysis.
A data warehouse maintains strict accuracy and integrity using a process called Extract, Transform, Load (ETL), which loads data in batches, porting it into the data warehouse’s desired structure.
Data warehouses provide a long-range view of data over time, focusing on data aggregation over transaction volume. The components of a data warehouse include online analytical processing (OLAP) engines to enable multi-dimensional queries against historical data.
Data warehouses applications integrate with BI tools like Tableau, Sisense, Chartio or Looker. They enable analysts using BI tools to explore the data in the data warehouse, design hypotheses, and answer them. Analysts can also leverage BI tools, and the data in the data warehouse, to create dashboards and periodic reports and keep track of key metrics.
Business Intelligence and Data Warehousing: Can You Have One Without the Other?
Two decades ago most organizations used decision support applications to make data-driven decisions. These apps queried and reported directly on data in transactional databases—without a data warehouse as an intermediary. This is similar to the current trend of storing masses of unstructured data in a data lake and querying it directly.
Colin White lists five challenges experienced back in the days of decision support applications, without a data warehouse:
- Data was not usually in a suitable form for reporting
- Data often had quality issues
- Decision support processing put a strain on transactional databases and reduced performance
- Data was dispersed across many different systems
- There was a lack of historical information, because transactional OLTP databases were not built for this purpose
These, among others, were the reasons almost all enterprises adopted the data warehouse model. All five of these problems still seem relevant today. So can we do without a data warehouse, while still enabling efficient BI and reporting?
BI and ETL: Running in a Data Lake without a Rigid ETL Process
With the advent of data lakes and technologies like Hadoop, many organizations are moving from a strict ETL process, in which data is prepared and loaded to a data warehouse, to a looser and more flexible process called Extract, Load, Transform (ELT).
Today ELT is mainly used in data lakes, which store masses of unstructured information, and technologies like Hadoop. Data is dumped to the data lake without much preparation or structure. Then, analysts identify relevant data, extract it from the data lake, transform it to suit their analysis, and explore them using BI tools.
Does the Data Lake Replace the Data Warehouse?
ELT is a workflow that enables BI analysis while sidestepping the data warehouse. But those same organizations that use Hadoop or similar tools in an ELT paradigm, still have a data warehouse. They use it for critical business analysis on their central business metrics—finance, CRM, ERP, and so on.
Data warehouses are still needed for the same five reasons listed above. Raw data must be prepared and transformed to enable analysis on the most critical, structured business data. If management needs to see a weekly revenue dashboard, or an in-depth analysis on revenue across all business units, data needs to be organized and validated; it can’t be pieced together from a data lake.
Can such a structured analysis happen without a rigid ETL process? Or in other words, are ELT strategies relevant inside the data warehouse?
BI in an Enterprise Data Warehouse without ETL
New, automated data warehouses are changing the game, by allowing Extract-Load-Transform (ELT) within an enterprise data warehouse. They can address:
- Data not in suitable form for reporting
- Data has quality issues
- Strain on transactional database performance
- Data dispersed across many systems
- Lack of historic information
The primary benefit is shorter time to analysis. With an automated data warehouse, you can go from raw data to analysis in minutes or hours, instead of weeks to months.
From Monolithic Data Warehouse to Agile Data Infrastructure
Data warehouses have come a long way. The monolithic Enterprise Data Warehouse (EDW), which required a multi-million dollar project to setup, and allowed only very limited BI analysis on specific types of structured data, is soon to be a thing of the past.
Today there are two quick, low cost ways to get from raw data to business insights:
- Data lake with an ELT strategy — does not allow the same critical business analysis as the EDW. But a data lake lets you do more with BI, extracting insights from enterprise data that was not previously accessible.
- Automated data warehouse — new ELT tools let you pull data into a cloud data warehouse, prepare and optimize the data automatically, and conduct transformations on the fly to organize the data for analysis. With a smart data warehouse and an integrated BI tool, you can literally go from raw data to insights in minutes.
The slow-moving ETL dinosaur is not acceptable in today’s business environment. Organizations are saving money and making business decisions faster, by simplifying and streamlining process the data preparation process.