Data Warehouse Tools

A data warehouse system gathers data from various sources and stores it in a single, central, consistent location to facilitate data analysis, data mining, artificial intelligence (AI), and machine learning. Data warehouses are purely meant for query and analytical purposes, and they typically hold vast volumes of historical data. The data stored inside a data warehouse is often obtained from a variety of sources, including application log files and transaction apps. To stay viable, organisations must rely on data and analytics. Dashboards, reports, and analytics tools are used by business users to derive data-driven insights, analyze business performance, and aid in decision making. These reports, dashboards, and analytics tools are powered by data warehouses, which store data efficiently to reduce data input and output (I/O) and provide query returns swiftly to thousands and thousands of users concurrently.

Data warehousing emerged in the 1980s with the creation of the Business Data Warehouse by IBM employees Paul Murphy and Barry Devlin. Inmon Bill, on the other hand, provided the true idea. He was regarded as the father of data warehouses. He has written on a number of themes related to the construction, operation, and upkeep of the warehouse and the Corporate Information Factory. A data warehouse was traditionally located on-premises—often on a mainframe computer—and its functionality was centred on obtaining data from external sources, cleaning and preparing the data, and loading and maintaining the data in a relational database. A data warehouse may now be stored on a dedicated appliance or in the cloud. A majority of data warehouses have included analytics capabilities as well as data visualisation and presentation tools.

Data Warehouse Architecture:

The architecture of a data warehouse is influenced by the demands of the company. A three-tier architecture is commonly used in data warehouses. The three layers in the architecture of a data warehouse are as follows:

• Bottom Tier: The bottom tier is made up of a data warehouse server, which is often a relational database system. It gathers, cleanses, and transforms data from multiple data sources using one of two methods: Extract, Transform, and Load (ETL) or Extract, Load, and Transform (ELT).
• Middle Tier: In the middle tier, we have the OLAP Server, which may be built in one of two ways:
  – By Relational OLAP (ROLAP) model – a relational database management system that is expanded. ROLAP converts multidimensional data operations to conventional relational operations.
  – By Multidimensional OLAP (MOLAP) model – immediately implements multidimensional data and operations
• Top Tier: The top tier is represented by a front-end user interface or reporting tool that allows end-users to perform ad-hoc data analysis on their company data. This layer contains the query and reporting tools, as well as the analysis and data mining tools.

How Is Data Processed in a Data Warehouse?

Data in a data warehouse can be processed either through batch processing or through real-time processing. Online analytic processing (OLAP) and online transaction processing (OLTP) are the two most frequent types of processing. OLAP processing is often done in batches. OLTP systems are designed for real-time processing and are not well suited to batch processing. When you isolate data processing from your OLTP system, you prevent data processing from interfering with your OLTP strain.

OLAP (online analytical processing) software is used to do multidimensional analysis on massive amounts of data from a unified, centralised data storage, such as a data warehouse, at incredibly fast speed. OLAP solutions are intended for multidimensional data analysis in a data warehouse including both historical and transactional data. Data mining and other business intelligence applications, complicated analytical computations, predictive scenarios, and also business reporting activities (including financial analysis, budgeting, and forecast planning) are all different uses of OLAP.

OLTP (online transactional processing) allows for the real-time execution of a large number of database transactions by a large number of individuals, generally through the internet. OLTP is intended to enable transactional applications by processing current transactions as quickly and correctly as feasible. ATMs, e-commerce software, credit card payment processing, ticket booking, reservation systems, and record-keeping tools are all examples of OLTP applications.

Popular Data Warehouse Tools:

Amazon Redshift

AWS Redshift is an Amazon Web Services data warehousing service. Redshift excels in handling massive amounts of data, with the capacity to process structured and unstructured data in the exabyte range. It, like many other AWS services, can be set up with a few clicks and offers a myriad of data input possibilities. Furthermore, Redshift data is constantly encrypted for further protection. Amazon Redshift has been one of the fastest-growing AWS Services since its debut in February 2013, with tens of thousands of clients across a wide range of industries and corporate sizes. NTT DOCOMO, FINRA, Johnson & Johnson, McDonald’s, Equinox, Fannie Mae, Hearst, Amgen, and NASDAQ are among the companies that have moved to Amazon Redshift.

Pros:

• The usage of MPP technology allows for unprecedented speed in producing results on massive data sets. Any other cloud service provider can not match the speed and low cost of AWS’s service.
• Amazon offers data encryption for any aspect of the Redshift operations. You, as the user, may choose which actions require encryption and which do not. Data encryption adds an extra degree of protection.
• Redshift includes features that allow you to automate actions that must be performed repeatedly. Administrative responsibilities such as creating daily, weekly, or monthly reports might fall under this category. It might be audits of resources and costs. It can also be done on a regular basis to clean up data.
• The cloud security is handled by Amazon, while the security of the apps within the cloud is the responsibility of the users. To provide an extra layer of protection, Amazon offers access control, data encryption, and a virtual private cloud.
• Amazon has an automatic and consistent backup of data on a regular basis. In the case of any problems, failures, or corruption, this can be utilised to restore. The backups are dispersed

Cons:

• When Redshift is utilised for data warehousing, indexing becomes a challenge. To index and store data, Redshift employs distribution and sort keys. To operate on the database, you must understand the ideas underlying the keys. AWS does not provide a solution for changing or managing keys with minimum understanding.
• OLAP databases (such as Redshift) are designed for analytical queries on enormous amounts of data. When compared to traditional OLTP (Online Transaction Processing) databases, OLAP has limitations when it comes to executing fundamental database functions. In OLAP databases, insert/update/delete operations have performance limits. It is frequently simpler to rebuild a table with modifications than it is to insert/update tables in Redshift. While OLAP works best with static data, OLTP databases perform better when it comes to data change.
• Redshift does not enable parallel upload for all databases. Redshift supports Amazon S3, EMR, and DynamoDB for concurrent uploads through ultra-fast MPP. To upload data from other sources, other scripts must be utilised. This might be a lengthy procedure.
• One of the fundamental foundations of a database is the need for uniqueness in data and the avoidance of duplication. AWS Redshift does not provide any tools or methods for ensuring data uniqueness. There will be duplicate data points if you migrate overlapping data from several sources to Redshift.

Microsoft Azure

In the middle of 2016, Azure made its Azure SQL Data Warehouse service broadly accessible for cloud data warehousing. This service has gone through various revisions since then, and Microsoft stated at the end of 2019 that the Azure SQL Data Warehouse service would be renamed as Azure Synapse Analytics. Azure Synapse Analytics is a cloud service that is very elastic and scalable. It is interoperable with a variety of other Azure solutions, including Data Factory and Machine Learning, as well as Microsoft products and SQL Server Tools. Through parallel processing, Azure’s SQL-based Data warehouse can handle massive amounts of data. It has addressed the majority of the problems of traditional data warehousing solutions since it is a distributed database management system. Azure SQL Data Warehouse distributes data over numerous shared storage and processing units before addressing the logic required in data queries. This makes it suited for massive data loading, transformation, and serving. It provides the same scalability and consistency as other Azure services, such as high-performance computing, as an integrated Azure feature.

Pros:

• Due to the separation of storage and compute components, the Azure data warehouse has a high degree of flexibility. Computing may be scaled on its own. Even while the query is executing, it enables resource addition and removal.
• Azure SQL has a number of security features (row-level security, data masking, encryption, auditing, etc.). Given the cyber dangers to cloud data security, Azure data warehouse components are secure enough to keep your data safe.
• Azure has a great level of scalability. The Azure data warehouse grows up and down easily based on the needs.
• Azure provides V12 portability. With the tools provided by Microsoft, you may quickly upgrade from SQL Server to Azure SQL and vice versa.

Cons:

• Synapse has the constraint of not allowing SQL users to do admin operations since they need T-SQL. There are also a number of T-SQL limitations that aren’t currently supported.
• Because Azure Synapse is restricted to one physical database because it utilises Azure SQL Database, you must rely on schemas to segregate your staging and DW tables.

Google BigQuery

Google’s serverless data warehouse is known as BigQuery. This scalable enterprise data solution is a cloud data warehouse that assists businesses in storing and querying data. Massive datasets may be uploaded to BigQuery machine learning to help you better comprehend your data. This software has access to Google’s infrastructure’s computing power. BigQuery, which includes machine learning, may assist you in processing and making sense of your data.

Pros:

• One of BigQuery’s most notable advantages is its ease of usage. Building your own data centre is not only costly but also time-consuming and difficult to scale. BigQuery simplifies the procedure. You enter your data into the programme and pay for only what you use. It’s a quick and easy solution to analyse and interpret your data without the hassle of constructing your own data centre.
• It is a fully managed platform that does not require downtime for upgrades and provides high availability and geo-redundancy automatically.
• BigQuery separates data storage and computation. This approach supports elastic scalability, which allows you to scale at a faster rate. It works effortlessly for real-time analytics and grows your data correctly to help you make sense of it.
• BigQuery secures your data and ensures its safety. Although you must have a disaster recovery strategy in place, this procedure alleviates the stress of having a disaster recovery plan in place in case your data is corrupted or destroyed.

Cons:

• It is most effective with flat tables, which might make managing an enterprise data model challenges.
• Queries that have not been performance-tuned, as well as queries that return a large amount of redundant data, can soon become expensive.

Snowflake

Snowflake is a cloud-native platform that removes the need for separate data warehouses, data lakes, and data marts, enabling safe data exchange throughout the business. It is built on the cloud infrastructures of Amazon Web Services, Microsoft Azure, and Google. Because there is no hardware or software to choose, install, configure, or administer, it is suitable for enterprises that do not want to devote resources to the setup, maintenance, and support of in-house servers.

Pros:

• Snowflake may be operated on Microsoft Azure, a cost-effective, scalable, and user-friendly cloud-based storage blob. It also has a big storage capacity, making it excellent for use by enterprises that deal with a lot of data.
• While conventional data warehouses required substantial investment in servers and other infrastructure, Snowflake provides far larger server capacity without the requirement for equipment upgrades. Everything in Snowflake is cloud-based, which means you can install it on a microscale that can subsequently be scaled up or down dependent on the needs of the firm.
• Snowflake’s backdrop provides IP whitelisting to restrict data access to just trustworthy, authorised users. Snowflake provides high-quality data security when combined with two-factor authentication, SSO authentication, and AES 256 encryption, as well as the fact that it encrypts data in transit and at rest.
• Snowflake’s design allows Snowflake users to share data. It also enables enterprises to share data with any data consumer, whether or not they are a Snowflake customer, via reader accounts that can be created straight from the user interface. This feature enables the provider to set up and administer a Snowflake account for a customer.

Cons:

• Snowflake now supports semi-structured and structured data. However, it is anticipated that unstructured data support will be added in the future.
• While Snowflake is extremely scalable and lets customers pay for just what they use, there are no data restrictions for both compute and storage. Many firms may find it all too simple to overuse their services, only to discover the error after billing.
• It might be difficult to move data from SQL Server to Snowflake. Snowflake provides Snowpipe for continuous data loading, however, it isn’t always the ideal option.

Micro Focus Vertica:

According to IT Central Station, Vertica, the most powerful SQL database analytics platform designed to solve the most demanding Big Data analytics endeavours, is the best cloud data warehouse option. Vertica provides the greatest performance at an extreme scale thanks to in-database Machine Learning and advanced analytics features, tight connectivity with open-source technologies like Hadoop, Kafka, and Spark, and an ecosystem-friendly MPP architecture.

Pros:

• Vertica’s Unified Analytics Warehouse enables you to integrate data silos that are expanding at an exponential rate—without relocating the data.
• Vertica SQL is resilient and powerful, and it is certified to function with all of your tools, not just those from your major vendor or those confined to a particular infrastructure.
• Vertica in Eon Mode controls dynamic workloads, allowing you to spin up storage and computing resources as needed and then spin them down to save money.

Cons:

• Does not support the enforcement of foreign keys or referential integrity.
• A lack of technical community assistance, according to around 50% of users, makes adoption difficult.

Teradata:

Teradata is an integrated platform that enables the storage, access, and analysis of business data on both cloud and on-premise infrastructure. Teradata Database is a system for storing information. It also supports a variety of tools and utilities, making it a fully functional and active relational database management system. Using Teradata’s capabilities, users may analyse any data and deploy it wherever.

Pros:

• Teradata is scalable in a linear fashion. It may increase database capacity simply by adding extra nodes to the existing database. If the volume of data increases, more hardware can be added to enhance the database capacity.
• Teradata has a large parallel processing capability and can accommodate numerous concurrent users and ad-hoc queries.
• It works well with a variety of Business Intelligence technologies.

Cons:

• A substantial quantity of Memory is necessary for storing, retrieving, and processing huge amounts of data. But every now and then insufficient memory results occur from a variety of factors.
• Data migration from Teradata RDBMS to another RDBMS is really challenging.
• As Memory becomes less accessible, the performance of its operation steadily degrades.

Amazon DynamoDB

Amazon offers DynamoDB, a fully managed proprietary NoSQL database service that supports key-value and document data formats, as part of the Amazon Web Services portfolio. DynamoDB provides a comparable data architecture to Dynamo and gets its name from it, but it has a distinctive underpinning implementation.

Pros:

• DynamoDB allows you to auto-scale by monitoring how close your consumption is to the top boundaries. This allows your system to react based on the quantity of data flow, allowing you to prevent performance difficulties while lowering expenditures.
• Developers can use the Time-to-Live feature to keep track of expired data and remove it automatically. This approach also aids in decreasing storage and the expenses associated with manual data erasure activities.
• DynamoDB’s fine-grained access control gives the table owner greater control over the data in the table.

Cons:

• Only available on AWS and cannot be installed on individual computers or servers.
• Data querying is quite restricted.
• Costs are more difficult to forecast. Because you’re paying per consumption, it’s frequently difficult to forecast when that usage will peak. While DynamoDB provides a lot under their free tier price, tiny errors can quickly deplete that.

APostgreSQL

PostgreSQL is an open-source relational database programme that runs on the Linux platform and works as a relational component in the DB system with objects. It is also known as Postgres because it accesses data in the database tables using Structured Query Language (SQL). For flexibility and SQL compliance, it supports both SQL and JSON for relational and non-relational queries. PostgreSQL enables complex data types and performance optimization techniques that are only found in costlier commercial databases such as Oracle and SQL Server.

Pros:

• PostgreSQL requires less maintenance and management for both embedded and business applications.
• It may be used as a geospatial data store for location-based applications and geographic information systems since it supports geographic objects.
• Its source code is accessible for free under an open-source licence. This gives you the ability to use, change, and apply it as needed for your business.
• It supports ACID, which stands for Atomicity, Consistency, Isolation, and Durability.

Cons:

• It is not controlled by a single group. As a result, although being fully featured and similar to other DBMS systems, it has had difficulty establishing a brand for itself.
• Changes for performance enhancement need more work than in MySQL since PostgreSQL focuses on compatibility.
• It is slower than MySQL in terms of performance.

Amazon RDS

Amazon Relational Database Service (Amazon RDS) is a web service that allows you to develop, deploy, and scale a cloud database. It provides a straightforward, cost-effective, and standardised method for managing basic database management chores. It was created to make the development, operation, administration, and scalability of a relational database for use as an application backend as simple as possible. AWS first offered the RDS service in October 2009, with MySQL support.

Pros:

• In the case of failure of a primary instance, you can generate a backup. This will be an asynchronous backup instance with the original copy. This will cut down on downtime when doing maintenance or updates.
• Amazon RDS allows you to effortlessly scale-up storage space, CPU, and other features. This function is not accessible in physical data centres or machines.
• RDS makes it simple to backup and patch software versions because most processes are automated.

Cons:

• You can only use RDS services. Amazon prohibits access to any database instance or shell.
• RDS databases do not have superusers or full root rights.
• Vendor lock-in is a result of the complicated and time-consuming procedure of extracting data from MySQL RDS.
• There is less visibility into your database’s performance.

Amazon S3

Amazon Simple Storage Service (Amazon S3) is Amazon Web Services’ most fundamental and worldwide Infrastructure as a Service (IaaS) offering (AWS). Amazon S3 has management options that allow you to optimise, organise, and configure data access to fit your unique business, organisational, and compliance needs. Amazon S3 allows its varied customers from different sectors to store and safeguard any amount of data for a number of use cases, including data lakes, websites, mobile apps, backup and restore, archiving, business applications, IoT devices, and big data analytics.

Pros:

• It comes with a plethora of documentation. There are not just API Reference manuals, but also lessons and videos to help you complete any activity.
• Over the course of a year, Amazon S3 guarantees 99.999999999% object durability. This means that data may be saved even if two data centres fail at the same time.
• With Amazon S3, you have several cost-effective alternatives for Cloud Data Migration, and it is extremely straightforward to move a significant volume of data to or from Amazon S3.
• Amazon S3 features a very user-friendly web interface that automates the tedious tasks of maintaining security, optimising storage classes, and managing data transport in the most efficient manner.

Cons:

• Changes to a replica require time to propagate to the other copies, and the object storage is inconsistent throughout this process. When listing, reading, updating, or deleting files, inconsistency issues arise.
• Some file operations are not supported. Some file operations required by Apache HBase, in particular, are not accessible, hence HBase cannot be operated atop Amazon S3.

SAP HANA

SAP HANA has a data warehouse solution called SAP BW/4HANA. This business warehousing system is built on an ABAP framework based on NetWeaver. It is a model-based approach in which the user creates a data warehouse model and data flow from which data is retrieved from the original data source.

Pros:

• You may modify the application based on your needs and use processed data from the data warehouse. It can perform sophisticated analytics and include machine learning skills.
• It allows for high-volume, real-time data processing.
• Access real-time or historical data from SAP or non-SAP data sources.

Cons:

• SAP HANA is only compatible with SAP or SUSE Linux certified hardware. Because licencing rates are so costly, it poses a problem for all users who wish to run it on any other type of hardware.
• Using hybrid HANA solutions, such as operating it partially in the cloud and partly on-premises, produces a slew of problems and renders the system inoperable.

MarkLogic

MarkLogic is an American software company that creates and sells an enterprise NoSQL database. Because of its capacity to store, manage, and search JSON and XML documents, as well as its semantic data format, it is classified as a multi-model NoSQL database. MarkLogic supports ACID consistency for transactions and offers a security architecture that is Common Criteria certified, as well as disaster recovery with high availability. It is intended to run on-premises in public or private cloud computing settings such as Amazon Web Services.

Pros:

• The Enterprise NoSQL database platform from MarkLogic is built to handle new data types and analytic requirements quickly. It provides flexibility in data models for adaptability and less administrative work.
• MarkLogic enables complete and adaptable data virtualization, including support for today’s information sources’ volume, diversity, velocity, and complexity.
• As data and/or user demands rise, MarkLogic uses a “shared-nothing” design to scale out a cluster using commodity hardware.

Cons:

• Licencing cost is a bit high.
• It requires a lot of space to store data.

MariaDB

MariaDB is a true open-source MySQL distribution. It was founded in the aftermath of Oracle’s acquisition of MySQL when several of the database’s key developers were afraid that Oracle would compromise the open-source concept. The developers of MariaDB ensure that each release is fully compatible with the matching version of MySQL. MariaDB not only utilises the same data and table definition files like MySQL, but it also employs the same client protocols, client APIs, ports, and sockets. The objective is to make it as easy as possible for MySQL users to migrate to MariaDB.

Pros:

• MariaDB is backwards compatible, which means that the most recent version is compatible with previous ones. This is a critical feature, given that it is open-source software that is regularly updated by the community.
• Because of MariaDB’s regular security releases, many users prefer it over MySQL. While this does not necessarily imply that MariaDB is safer, it does show that the development community is concerned about security.
• MariaDB has been performance-optimised and is far more powerful than MySQL for huge data collections. Another advantage is the easy conversion from other database systems to MariaDB.

Cons:

• Beginning with version 10.2, MariaDB only supports JSON data types. Even so, it’s only an alias for LONGTEXT that’s there for compatibility.
• Some capabilities are accessible solely in the MySQL Enterprise Edition. These capabilities are not available in MariaDB.
• MariaDB has a tendency to bloat. Its primary IDX log file, in particular, grows exceedingly huge after extended use, reducing performance.

Cloudera

The Cloudera Data Warehouse (CDW) service is a managed data warehouse that uses a containerized architecture to operate Cloudera’s strong engines. It is a component of the new Cloudera Data Platform, or CDP, which debuted on Microsoft Azure earlier this year. The CDW service enables you to achieve SLAs, onboard new use cases with minimal friction, and save costs.

Pros:

• Aside from the compute and storage separation you’d expect from a cloud data warehouse service, CDW also offers automatic provisioning/scaling/shrinking of its computing resources. This allows for high degrees of flexibility, which increases resource utilisation and decreases costs significantly.
• Impala and HIVE LLAP are two very high-performance MPP query engines available at CDW. These engines have a stellar track record of powering mission-critical data warehouses in a variety of demanding environments.
• Impala and Hive engines from CDW directly operate on highly compressed open file formats like Parquet and ORC. These file formats not only assist to minimise data duplication into exclusive storage formats, but they also provide extremely efficient storage formats.

Cons:

• The costs keep on changing
• Log visibility is an issue.

IBM DB2:

IBM DB2 refers to a group of relational database management systems (RDBMS). DB2, which was first commercially published in 1983, provides its clients with a way to manage structured and unstructured data that is kept both on-premises and in the cloud. These hybrid data management technologies use AI capabilities to produce an effective way of giving data insights that is both adaptable and scalable.

Pros:

• DB2’s Structured Query Language (SQL) dialect is more powerful than Microsoft’s SQL offering. DB2 has features including object tables, before triggers, Java method compatibility, numerous user-defined methods, and array support. MS SQL does not support any of these capabilities.
• IBM creates DB2 versions that work on all available systems, not just Windows-based platforms. AIX, HP-UX, Linux, and Sun are among the systems supported by DB2.
• The fact that DB2 is an IBM product is a significant benefit. DB2, which was created many years ago at IBM’s database labs, has added feature after feature over the years. After extensive testing, IBM releases software updates and fixes on a regular basis.

Cons:

• DB2 configuration can be time-consuming.
• There is a danger that queries will return incorrect results if DB2 does not communicate appropriately with other products.

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