#olap*

[govindhtech]

5th Gen Intel Xeon Scalable Processors Boost SQL Server 2022

5th Gen Intel Xeon Scalable Processors

While speed and scalability have always been essential to databases, contemporary databases also need to serve AI and ML applications at higher performance levels. Real-time decision-making, which is now far more widespread, should be made possible by databases together with increasingly faster searches. Databases and the infrastructure that powers them are usually the first business goals that need to be modernized in order to support analytics. The substantial speed benefits of utilizing 5th Gen Intel Xeon Scalable Processors to run SQL Server 2022 will be demonstrated in this post.

OLTP/OLAP Performance Improvements with 5th gen Intel Xeon Scalable processors

The HammerDB benchmark uses New Orders per minute (NOPM) throughput to quantify OLTP. Figure 1 illustrates performance gains of up to 48.1% NOPM Online Analytical Processing when comparing 5th Gen Intel Xeon processors to 4th Gen Intel Xeon processors, while displays up to 50.6% faster queries.

The enhanced CPU efficiency of the 5th gen Intel Xeon processors, demonstrated by its 83% OLTP and 75% OLAP utilization, is another advantage. When compared to the 5th generation of Intel Xeon processors, the prior generation requires 16% more CPU resources for the OLTP workload and 13% more for the OLAP workload.

The Value of Faster Backups

Faster backups improve uptime, simplify data administration, and enhance security, among other things. Up to 2.72x and 3.42 quicker backups for idle and peak loads, respectively, are possible when running SQL Server 2022 Enterprise Edition on an Intel Xeon Platinum processor when using Intel QAT.

The reason for the longest Intel QAT values for 5th Gen Intel Xeon Scalable Processors is because the Gold version includes less backup cores than the Platinum model, which provides some perspective for the comparisons.

With an emphasis on attaining near-real-time latencies, optimizing query speed, and delivering the full potential of scalable warehouse systems, SQL Server 2022 offers a number of new features. It’s even better when it runs on 5th gen Intel Xeon Processors.

Solution snapshot for SQL Server 2022 running on 4th generation Intel Xeon Scalable CPUs. performance, security, and current data platform that lead the industry.

SQL Server 2022

The performance and dependability of 5th Gen Intel Xeon Scalable Processors, which are well known, can greatly increase your SQL Server 2022 database.

The following tutorial will examine crucial elements and tactics to maximize your setup:

Hardware Points to Consider

Choose a processor: Choose Intel Xeon with many cores and fast clock speeds. Choose models with Intel Turbo Boost and Intel Hyper-Threading Technology for greater performance.

Memory: Have enough RAM for your database size and workload. Sufficient RAM enhances query performance and lowers disk I/O.

Storage: To reduce I/O bottlenecks, choose high-performance storage options like SSDs or fast HDDs with RAID setups.

Modification of Software

Database Design: Make sure your query execution plans, indexes, and database schema are optimized. To guarantee effective data access, evaluate and improve your design on a regular basis.

Configuration Settings: Match your workload and hardware capabilities with the SQL Server 2022 configuration options, such as maximum worker threads, maximum server RAM, and I/O priority.

Query tuning: To find performance bottlenecks and improve queries, use programs like Management Studio or SQL Server Profiler. Think about methods such as parameterization, indexing, and query hints.

Features Exclusive to Intel

Use Intel Turbo Boost Technology to dynamically raise clock speeds for high-demanding tasks.

With Intel Hyper-Threading Technology, you may run many threads on a single core, which improves performance.

Intel QuickAssist Technology (QAT): Enhance database performance by speeding up encryption and compression/decompression operations.

Optimization of Workload

Workload balancing: To prevent resource congestion, divide workloads among several instances or servers.

Partitioning: To improve efficiency and management, split up huge tables into smaller sections.

Indexing: To expedite the retrieval of data, create the proper indexes. Columnstore indexes are a good option for workloads involving analysis.

Observation and Adjustment

Performance monitoring: Track key performance indicators (KPIs) and pinpoint areas for improvement with tools like SQL Server Performance Monitor.

Frequent Tuning: Keep an eye on and adjust your database on a regular basis to accommodate shifting hardware requirements and workloads.

SQL Server 2022 Pricing

SQL Server 2022 cost depends on edition and licensing model. SQL Server 2022 has three main editions:

SQL Server 2022 Standard

Description: For small to medium organizations with minimal database functions for data and application management.

Licensing

Cost per core: ~$3,586.

Server + CAL (Client Access License): ~$931 per server, ~$209 per CAL.

Basic data management, analytics, reporting, integration, and little virtualization.

SQL Server 2022 Enterprise

Designed for large companies with significant workloads, extensive features, and scalability and performance needs.

Licensing

Cost per core: ~$13,748.

High-availability, in-memory performance, business intelligence, machine learning, and infinite virtualization.

SQL Server 2022 Express

Use: Free, lightweight edition for tiny applications, learning, and testing.

License: Free.

Features: Basic capability, 10 GB databases, restricted memory and CPU.

Models for licensing

Per Core: Recommended for big, high-demand situations with processor core-based licensing.

Server + CAL (Client Access License): For smaller environments, each server needs a license and each connecting user/device needs a CAL.

In brief

Faster databases can help firms meet their technical and business objectives because they are the main engines for analytics and transactions. Greater business continuity may result from those databases’ faster backups.

Read more on govindhtech.com

[openbooth]

Announcing DuckDB 1.0.0 To install the new version, please visit the installation guide. For the release notes, see the release page.

— https://ift.tt/YCOVSum

[muellermh]

14. Wie wird Amazon Redshift für Cloud Computing verwendet?: "MHM Digitale Lösungen UG: Wie Sie Amazon Redshift für Cloud Computing effizient nutzen können"

#CloudComputing #AmazonRedshift #Performance #Storage #Analytics #OLAP #Datenbanken #Datenmanagement #Skalierbarkeit #Sicherheit

Cloud Computing ist mittlerweile ein zentraler Bestandteil vieler Unternehmen. Mit Amazon Redshift können alle möglichen Cloud Computing Anwendungen durchgeführt werden. Amazon Redshift ist eine skalierbare Cloud-basierte Datenbank, die als Massenspeicher- und Analyseplattform für unternehmensinterne und externe Daten dienen kann. Es verfügt über eine Reihe hochentwickelter Funktionen, die es…

[rexavki]

Clickhouse : OLAP vs OLTP ???

[omggadgets]

Understanding OLAP: The Power of Online Analytical Processing

OLAP, or Online Analytical Processing, is a technology that allows users to analyze large, complex data sets in real time. It enables users to perform complex queries and data analysis on data sets from multiple sources quickly and easily. If you are wondering what is OLAP, OLAP systems are designed to help users make informed decisions by providing timely and accurate data that can be easily…

[snowflakemasters]

OLAP and OLTP Difference

In today's data-driven world, businesses rely on robust database systems to store, manage, and analyze vast amounts of information. Among these systems, OLAP (Online Analytical Processing) and OLTP (Online Transactional Processing) play crucial roles, each serving distinct purposes in database management. 

This article aims to uncover the critical variances between OLAP and OLTP, comprehensively understanding their definition, concepts, diverse perspectives, relevant statistics, and real-world examples. By grasping these fundamental differences, organizations can make informed decisions when selecting the most suitable database system to meet their needs. 

So, let's dive into the intriguing world of OLAP and OLTP and explore how they shape the landscape of database management.

What are OLAP and OLTP?

OLAP (Online Analytical Processing):

OLAP systems typically store data in a denormalized format, which means that data is organized into a structure optimized for analysis rather than transactional processing. This denormalized structure allows for faster query performance and supports complex analytical operations across multiple dimensions. OLAP systems often use specialized databases and storage technologies to efficiently manage and query large volumes of data, enabling users to perform sophisticated analysis tasks with ease.

In contrast, OLTP systems typically store data in a normalized format, which means that data is organized into tables with minimal redundancy to ensure data integrity and reduce storage space. Normalization helps optimize the efficiency of transactional operations by minimizing data duplication and improving data consistency. OLTP systems often prioritize fast read and write operations, with a focus on maintaining data integrity and ensuring the accuracy of transactions in real-time.

OLTP (Online Transactional Processing):

OLTP, which stands for Online Transactional Processing, is a database technology primarily focused on real-time transactional operations. It handles day-to-day transactional tasks such as inserting, updating, and deleting records in a database. OLTP systems are commonly used in applications that require immediate and reliable transaction processing, such as e-commerce platforms, banking systems, and airline reservation systems. 

The main characteristics of OLTP systems include high concurrency, low response times, data integrity, and ACID (Atomicity, Consistency, Isolation, Durability) compliance. Unlike OLAP systems optimized for complex analysis, OLTP systems are designed for write-intensive operations, processing numerous small transactions concurrently.

Key Differences between OLAP and OLTP:

Data Processing Approach: OLAP follows a multidimensional data model, employing queries to analyze and aggregate data from various perspectives. On the other hand, OLTP adopts a relational model, emphasizing real-time transaction processing and maintaining data integrity.

Database Structure: OLAP systems typically utilize a star, snowflake, or hybrid schema for optimal analytical performance. Conversely, OLTP systems employ normalized schemas to eliminate redundancy and support efficient transactional operations.

User Interaction: OLAP systems provide a user-friendly interface that enables end-users to interactively navigate and explore data through features like drill-down, slice-and-dice, and pivoting. In contrast, OLTP systems primarily facilitate standard CRUD (Create, Read, Update, Delete) operations, focusing on quick response times for concurrent transactions.

Performance Requirements: OLAP systems prioritize complex queries and aggregations, often dealing with large datasets. Therefore, they require significant processing power, memory, and storage capabilities. On the other hand, OLTP systems prioritize quick and reliable transaction execution, necessitating high throughput and low response times.

Diverse Perspectives: Industry Applications and Examples:

OLAP in Business Intelligence: Many enterprises leverage OLAP to gain actionable insights from their operational data, enabling informed decision-making and strategic planning. Companies like Amazon and Walmart utilize OLAP for sales analysis, inventory management, and demand forecasting.

OLTP in E-commerce: OLTP plays a vital role in e-commerce platforms, facilitating real-time online transactions, inventory management, and secure payment processing. For instance, platforms like eBay and PayPal rely on OLTP systems to handle high volumes of concurrent transactions.

OLAP vs. OLTP in Finance: In the finance sector, OLAP empowers banks and financial institutions to perform in-depth analysis, risk assessment, and portfolio optimization. In contrast, OLTP ensures secure and accurate execution of financial transactions backed by fraud detection mechanisms.

Relevant Statistics and Research Findings:

According to a report by Gartner, the adoption rate of OLAP and OLTP systems has shown a steady increase in recent years. The survey found that.

78% of organizations utilize OLAP systems for complex data analysis.

87% of organizations have implemented OLTP systems for day-to-day transactional processing.

Benefits of Leveraging OLAP and OLTP Systems

A study by the International Data Corporation (IDC) highlighted the benefits organizations could experience by effectively leveraging OLAP and OLTP systems. The findings reveal that organizations that harness the power of these systems can achieve : 

Higher profitability: By utilizing OLAP and OLTP systems, organizations can gain valuable insights from historical data, enabling better decision-making and strategic planning. These, in turn, can lead to improved profitability.

Improved decision-making capabilities: OLAP systems allow users to perform complex analysis, data mining, and trend analysis, providing decision-makers with accurate and timely information. On the other hand, OLTP systems provide real-time transactional processing, enabling immediate and reliable execution of critical business transactions.

Case Studies: Successful Implementations of OLAP and OLTP:

Case Study 1: Company XYZ Improves Decision-Making with OLAP:

Company XYZ, a multinational retail corporation, implemented an OLAP system to analyze sales data across various dimensions. They gained deep insights into customer behaviour, product performance, and market trends using OLAP's drill-down and slice-and-dice capabilities. That empowered the company to make data-driven decisions, leading to optimized inventory management, targeted marketing campaigns, and increased sales revenue.

Case Study 2: E-commerce Platform Boosts Customer Satisfaction with OLTP:

An e-commerce platform faced challenges handling a high volume of transactions, resulting in slow response times and customer dissatisfaction. Implementing a robust OLTP system improved performance, reducing transaction processing time by 50%. As a result, customers experienced seamless purchasing experiences that led to increased customer satisfaction and repeat business.

Advantages and Limitations of OLAP and OLTP

Advantages of OLAP:

Powerful data analysis capabilities

Flexibility in exploring data from multiple perspectives

Support for complex queries and aggregations

Decision-making support through insights and patterns

Limitations of OLAP:

High resource requirements (processing power, memory, storage)

Longer response times for complex queries

Limited real-time data availability

Advantages of OLTP:

Efficient transaction processing

Data integrity and consistency

High concurrency support

Real-time data availability

Limitations of OLTP:

Limited analytical capabilities

Difficulty handling complex queries and aggregations

Higher maintenance overhead for data consistency

Benefits of OLAP vs OLTP

OLAP and OLTP systems offer distinct benefits for organizations based on their specific needs and use cases.

Computational automation: OLAP systems allow for automated processing of complex data structure computations, reducing the need for manual calculations.

Data mining: OLAP systems can extract valuable insights and patterns from large datasets.

Trend analysis: OLAP systems enable organizations to analyze historical data trends and make informed decisions based on past patterns and behaviours.

Real-time transaction processing: OLTP systems excel at processing real-time or near real-time transactions, allowing immediate updates and smooth customer interactions.

Efficient handling of large data volumes: OLTP systems are designed to handle high volumes of data efficiently, making them ideal for transactional processing in industries such as retail and finance.

Consistency and data integrity: OLTP systems prioritize maintaining data consistency and integrity, ensuring that transactions are accurately recorded and maintained.

It is vital for organizations to carefully evaluate their specific business requirements, data analysis needs, performance considerations, and scalability requirements to determine the most suitable system for their operations. In some cases, a combination of OLAP and OLTP systems may be ideal, as they serve different purposes and can complement each other to meet various organizational needs.

OLTP vs OLAP examples

Here are some examples to illustrate the differences between OLTP and OLAP:

E-commerce Platform: An e-commerce website that allows customers to search for products, add items to their cart, and complete purchases is an example of an OLTP system. It processes numerous small transactions in real-time, such as order placement, inventory updates, and payment processing.

Banking System: A banking system that handles daily transactions like deposits, withdrawals, transfers, and balance inquiries is another example of an OLTP system. It ensures the integrity and consistency of financial data across multiple accounts and processes transactions in real-time.

Business Intelligence Reporting: An organization using an OLAP system to generate complex reports and perform data analysis for decision-making purposes exemplifies an OLAP use case. These reports may involve aggregating large volumes of historical sales data, performing trend analysis, and identifying patterns or correlations.

Data Mining and Analytics: A retailer analyzing customer buying patterns, product sales across regions, and customer segmentation using an OLAP system is another example of OLAP usage. That involves querying and analyzing large volumes of data from multiple dimensions to gain insights and make data-driven decisions.

These examples demonstrate how OLTP and OLAP systems serve different purposes in real-world applications, with OLTP handling real-time transactional tasks and OLAP enabling advanced data analysis and reporting.

Factors to Consider in Choosing between OLAP and OLTP:When deciding between OLAP and OLTP systems, organizations should consider several factors. These factors include:

Nature of the business: It's essential to understand the heart of the company and the type of data that will be processed. That includes the volume, complexity, and type of data the system will handle.

Data analysis requirements: Organizations should also consider the type of analysis required, whether it's simple transactional processing or complex data mining and trend analysis.

Performance needs: Performance is a critical factor to consider based on the size of the data that needs to be processed, as this significantly impacts the processing speed of the system.

Scalability: Organizations should consider if the system is scalable and can accommodate future needs as a business grows.

It's essential to assess the specific goals and objectives when deciding between OLAP and OLTP systems. While an organization might require OLAP systems for complex data analysis, it might also need OLTP systems for day-to-day transactional processing. Therefore, combining both methods may be an ideal solution for meeting different needs. Careful consideration of these factors can lead to selecting the right system that suits an organization's needs, leading to optimal utilization of resources and increased efficiency.

Conclusion:

In conclusion, understanding the critical variances between OLAP and OLTP is essential for organizations seeking to leverage database systems effectively. Whether making strategic decisions based on historical data or processing real-time transactions, selecting the appropriate system can significantly impact a company's success. 

By considering diverse perspectives, analyzing relevant statistics, and exploring real-world case studies, businesses can confidently choose between OLAP and OLTP to maximize the value of their data.

[oditeksolutionsyaass]

Crystal Reports Migration to Jasper! OdiTek's Jaspersoft reporting, migrating, consulting services enables enterprise with data-driven decision making.

[kyligenc]

OLAP On AWS | Kyligence Cloud-Native Big Data Solution

Olap Aws users manage, analyze, and get the most from their cloud data assets with higher performance and lower cost.

[sprinkledata12]

Top 21 ETL Tools For 2023

In this digital world, everyday great volumes of data are being generated from varied sources, and companies want to give this data a form and structure by assembling it in an organized way and at a unified place to use it to their advantage. They want to analyze their data further to get a good understanding and to make well-informed data-driven decisions. To bring meaning to this raw data, ETL tools play a significant role and help businesses to take data analytics to the next level.

There are several ETL tools available in the market which automate this whole process of building data-pipeline, managing and monitoring them. In this article, we will understand the whole process of ETL in detail and ETL tools that are best suited to automate your data pipelines for accurate analysis.

What is ETL?‍

ETL stands for “Extract, Transform and Load”. ETL is a process of extracting data from different data sources, cleansing and organizing it, and eventually, loading it to a target data warehouse or a Unified data repository.

Why ETL?

In today's data-centric world ETL plays a vital role in maintaining the integrity of a company by keeping its data up to date. To get the correct insight it is therefore important to perform ETL mainly due to the following reasons:

1. Data Volumes: The generated data has very high volume and velocity as many organizations have historical as well as real-time data being forged continuously from different sources.

2. Data Quality: The quality of the generated data is not exemplary as data is present in different formats like online feeds, online transactions, tables, images, excel, CSV, JSON, text files, etc. Data can be structured or unstructured, so to bring all different data formats to one homogeneous format performing the ETL process is highly needed.

To overcome these challenges many ETL tools are developed that make this process easy and efficient and help organizations combine their data by going through processes like de-duplicating, sorting, filtering, merging, reformatting, and transforming to make data ready for analysis.

ETL in detail:

1. Extract:

Extract is the first step of the ETL process that involves data being pulled from different data sources. It can extract data from the following sources listed below - 

Data Storage Platform & Data warehouses

Analytics tool

On-premise environment, hybrid, and cloud

CRM and ERP systems

Flat files, Email, and Web Pages

Manual data extraction can be highly time-consuming and error-prone, so to overcome these challenges automation of the Extraction process is the optimal solution.

Data Extraction: Different ways of extracting data.

1.1. Notification-based

In Notification-based extraction whenever data is updated, a notification is generated either through data replication or through webhooks (SaaS application). As soon as the notification is spawned data is pulled from the source. It is one of the easiest ways to detect any update but is not doable for some data sources that may not support generating a notification.

1.2. Incremental Extraction

In incremental extraction, only records that have been altered or updated are extracted/ingested. This extraction is majorly preferred for daily data ingestion as low-volume data is transferred making the daily data extraction process efficient. One major drawback of this extraction technique is once the extracted data is deleted it may not be detected.

1.3. Complete data extraction

In complete data extraction, the entire data is loaded. If a user wants to get full data or to ingest data for the first time then complete data extraction is preferred. The problem with this type of extraction is that if the data volume is massive it can be highly time-consuming.

Challenges in Data Extraction:

Data extraction is the first and foremost step in the ETL process, so we need to ensure the correctness of the extraction process before proceeding to the next step.

Data can be extracted using SQL or through API for SaaS, but this way may not be reliable as the API may change often or be poorly documented and different data sources can have various APIs. This is one of the major challenges faced during the data extraction process, other challenges are mentioned below.

Changing data formats

Increasing data volumes

Updates in source credentials.

Data issue with Null values

Change requests for new columns, dimensions, derivatives, and features.

2. TRANSFORM

Transform is the second step of the ETL process, in this raw data undergoes processing and modifications in the staging area. In this process, data is shaped according to the business use case and the business requirements.

The transformation layer consists of some of the following steps:

Removing duplicates, cleaning, filtering, sorting, validating, and affirming data.

Data inconsistencies and missing values are determined and terminated.

Data encryption or data protection as per industrial and government rules is implemented for security.

Formatting regulations are applied to match the schema of the target data repository

Unused data and anomalies are removed

Data Transformation: Different ways of transforming data

2.1. Multistage Data Transformation –

In multistage data transformation, data is moved to an intermediate area or staging area where all the transformation steps take place then eventually data is transferred to the final data warehouse where the business use cases are implemented for better decision-making.

2.2. In-Warehouse Data Transformation –

In ‘In-Warehouse Data Transformation ’,‍ data is first loaded into the data warehouse, and then all the subsequent data transformation steps are performed. This approach of transforming data is followed in the ELT process.

Challenges in Data Transformation

Data transformation is the most vital phase of the ETL process as it enhances data quality and guarantees data integrity yet there are some challenges faced when transforming data comes into play. Some challenges faced in transforming data are mentioned below:

Increasing data volumes makes it difficult to manage data and any transformation made can result in some data loss if not done properly.

The data transformation process is quite time-consuming and the chances of errors are also very high due to the manual effort.

More manpower and skills are required to efficiently perform the data transformation process which may even lead businesses to spend high.

3. LOAD

Once data is transformed, it is moved from the staging area to the target data warehouse which could be on the cloud or on-premise. Initially, the entire data is loaded, and then recurring loading of incremental data occurs. Sometimes, a full fetch of data takes place in the data warehouse to erase and replace old data with new one to overcome data inconsistencies.

Once data is loaded, it is optimized and aggregated to improve performance. The end goal is to quicken up the query span for the analytics team to perform accurate analysis in no time.

Data Loading: Considerations for error-free loading

Referential integrity constraint needs to be addressed effectively when new rows are inserted or a foreign key column is updated.

Partitions should be handled effectively for saving costs on data querying.

Indexes should be cleared before loading data into the target and rebuilt after data is loaded.

In Incremental loading, data should be in synchronization with the source system to avoid data ingestion failures.

Monitoring should be in place while loading the data so that any data loss creates warning alerts or notifications.

Challenges in Data Loading:

Data loading is the final step of the ETL process. This phase of ETL is responsible for the execution of correct data analysis. Therefore one must ensure that the data quality is up to the mark. The main challenge faced during data loading is mentioned below:

Data loss – While loading the data into the target system, there might be API unavailability, network congestion/failure or API credentials may expire these factors can result in complete data loss posing a greater threat to the business.

Overall Challenges of ETL

1. Code Issues

If ETL pipeline code is not optimized or manually coded, then such inefficiencies might affect the ETL process at any stage: It may cause problems while extracting data from the source, transforming data, or loading data into the target data warehouse and backtracking the issue can even be a tedious task.

2. Network Issues

The ETL process involves massive data transfer and processing on a daily basis which needs to be quick and efficient. So, the network needs to be fast and reliable, high latency of the network may create unexpected troubles in any of the stages and any network outage may even lead to data loss.

3. Lack of resources

Lack of any computing resources including storage, slow downloading, or lagging data processing in ETL may lead to fragmentation of your file system or create caches over a period of time.

4. Data Integrity

Since ETL involves collecting data from more than one source, if not done rightly, data might get corrupted which may create several inconsistencies and hence can cause data health reduction. So latest data needs to be carefully collected from sources and transformation techniques should be used accordingly.

5. Maintenance

In any organization increase in data corresponds to an increase in data sources so for business to maintain all their enormous data in a unified place more data connectors will keep on adding. So, while planning the ETL process, scalability, maintenance and the cost of maintenance should always be considered.

ETL vs ELT?

The main difference between ETL and ELT is the order of transformation, in ETL it happens before loading the data into the data warehouse however in ELT, data is first loaded and then its transformation takes place in the warehouse itself.

ELT Benefits over ETL

When dealing with high volumes of data ELT has a better advantage over ETL as transforming data before loading it into the data warehouse is an error-prone process and any mistake during transformation can cause complete data loss. Whereas in ELT data is first loaded into the warehouse and then it is transformed. So the chances of data loss are minimized in ELT as the data sits in the warehouse itself.

In ELT, not much planning is required by the team as compared to the ETL process. In ETL proper transformation rules need to be identified before the data loading process is executed which can be very time-consuming.

ELT is ideal for big data management systems and is adopted by organizations making use of cloud technologies, which is considered an ideal option for efficient querying.

For ETL, the process of data ingestion is very slow and inefficient, as the first data transformation takes place on a separate server, and after that data loading process starts. ELT does much faster data ingestion, as there is no data transfer to a secondary server for any restructuring. In fact, with ELT data can be loaded and transformed simultaneously.

ELT as compared to ETL is much faster, scalable, flexible, and efficient for large datasets which consist of both structured and unstructured data. ELT also helps to save data egress costs as before the transformation process the data sits in the data warehouse only.

Why do we need ETL tools?

ETL tools help to make the ETL process fast and efficient hence benefitting the businesses to stay one step ahead of their competitors. Some of the benefits of choosing the right ETL tool for your business are mentioned below:

1. Time Efficient: ETL tools permit us to collect, modify and integrate data automatically. Using ETL tools businesses can save more time as compared to time spent by bringing in data physically.

2. Low-code analysis: ETL tools generally offer low code/ no code functionality which helps to boost efficiency, requires less manual effort, and helps in keeping costs at bay.

3. Analyzing & Reporting: With the introduction of ETL tools analyzing data for reporting and dashboarding has become very easy. Data is available to us in a consolidated view and with the help of the right ETL tools, accurate analysis and reporting can be done.

4. Historical context: Businesses can benefit by analyzing the historical trends of their data to get its deep historical context and to predict some upcoming trends. There are many ETL tools available in the market that can efficiently analyze historical data in no time.

5. Data governance and ROI: It improves data accuracy and audit which is required for compliance with regulations and standards. It results in higher ROI for investments made in data teams.

There are numerous other benefits of ETL tools but the main challenge is to identify which ETL tool should be used by organizations to implement the right business use case according to their requirements.

Criteria for Choosing the Right ETL Tools

With the emergence of modern data-driven businesses, the space of ETL tools have also seen huge interest making the zone a crowded sector. But, with so many ETL Tools available in the market how should we go ahead with choosing the right ETL Tools for our businesses?

Below listed are some criteria that one should consider before choosing the right ETL tool according to the requirements.

1. In-built Connectors:

ETL tools with a high number of connectors should be preferred as they will provide more flexibility to businesses. Not only connectors but also, some of the widely used databases and applications in the industry must be available in the ETL tool that we choose.

2. Ease of Use and Clean User Interface:

ETL tools should be user-friendly, and an easy-to-understand user interface saves a lot of time and effort for its users and help them in using the tool hassle-free. Along with this, clear documentation to users should also be provided to get a better understanding of the selected ETL tool.

3. Scalable:

With the emergence of data-centric businesses, data tends to grow exponentially every day so to keep up with the cost a scalable ETL tool is of paramount importance for every business. We must choose an ETL tool with a good scalability option available to cater to business needs.

4. Error Handling:

Data Consistency and accuracy should be at the helm of any ETL tool that we choose for our business. In addition to this, the ETL tool should also have capabilities of smooth and efficient data transformation capabilities.

5. Real-time Data Ingestion and Monitoring:

ETL Tools with the capability to ingest data on a real-time basis from a wide range of sources should be highly considered for businesses that generate data every day. Apart from this, monitoring of the data ingestion and transformation process should be done accurately in that ETL tool to keep track of your data.

Check here Top 21 ETL Tools for 2023 : https://www.sprinkledata.com/blogs/etl-tools

[openbooth]

ClickHouse or StarRocks? Here is a Detailed Comparison While StarRocks and ClickHouse have a lot in common, there are also differences in functions, performance, and application scenarios. Check out this breakdown of both! A New Choice of Column DBMS Hadoop was developed 13 years ago.

— https://ift.tt/OuPeUFB

[stargazerbibi]

[ 13th june, 2023 • 56/100 days of uni ]

my part of the BD project is done. it's finally done! it took so long, but it's done. i think i'll need to make some changes in the OLAP queries, but the SQL queries should be golden👌 aside from that, it wasn't a very productive day and i didn't sleep well, but hopefully i'll be tired enough to go to bed early tomorrow 🩷🩷

[identifying-wrestling-moves]

OLAP/Reverse Palo Special

[snappedsky]

Fanatics 99.3

Gaz fights four aliens in the competition’s first battle.

For anyone who didn’t see my update post, I’ve decided to change my schedule. Instead of updating every other Saturday, Fanatics will now update every Saturday!

*Links to previous and next chapters in reblog*

--

Greatest in the Galaxy Part 3

“Hold him.”

Zim, Tak, and Pepito grab Dib, holding him still as Gaz leaves their sky box.

“You got this, Gaz,” Squee cheers from where he’s sitting at a table while Shmoopy looks over his legs.

“She can’t fight!” Dib exclaims, sick with worry.

“No, Dib, you’re mistaken,” Pepito argues, “Gaz fights all the time.”

“Y-yeah, but...but...”

“She has to fight, Dib,” Zim demands, “it’s for the competition.” “Yeah, so stop being such a baby!” Tak snaps.

Gaz ignores all of them as she heads down to the stadium grounds, her war hammer resting on her shoulder.

She emerges from the dimly lit corridors into the bright lights of the arena, surrounded by the cheering of the audience. She stares around, smiling with excitement.

“Look at her,” Kio says from their balcony, everyone else watching beside her. “She’s already loving this.” Dib grips the railing, whimpering uneasily.

Gaz and her four opponents- Tav of Irk, Olap of Swif’el, Wirez of Techon-3, and Peccs of Mus’ular- approach the middle of the ring and stand in a circle, glaring at each other.

“This is an all-out, anything-goes, free-for-all! Players are encouraged to not completely annihilate their opponents- this is a friendly competition after all- but don’t expect anyone to jump in if things start getting out of hand.” “Do people often get killed in these battles?” Squee asks as he limps over.

“Not often,” Zim replies, “but it’s not uncommon.” “How are your legs?” Pepito asks.

“I’ll be alright,” Squee smiles.

“Begin!”

Gaz flinches as everyone looks at her. Peccs swings his large fists; spider legs extend from Tav’s PAK and begin firing lasers; Olap rushes for her, claws unsheathing from his top paws; Wirez grabs a laser gun from his belt and fires.

Gaz jumps backwards, narrowly dodging all of the attacks.

“Oh! Just like last round, the returning players are ganging up on the newbie! How long will Gaz be able to hold out?”

Gaz races around the arena, dodging laser fire from Tav and Wirez and keeping out of range of Peccs and Olap. The onslaught is barely giving her time to think let alone retaliate.

“This isn’t fair!” Dib exclaims, “they can’t gang up on her! She’s just a sweet, little girl!”

“She’s neither of those things,” Squee argues.

“Knock it off with the older brother complex, Dib,” Pepito groans, “this is Gaz we’re talking about. If there’s one thing she can do, it’s fight dirty.”

Gaz takes a sudden left and Peccs skids to a stop. But as he starts to turn after her, lasers hits his back.

“Whoops,” Wirez grunts, lowering his gun.

“Watch it, Techon,” Peccs snarls, “or I’ll crush you.”

“You stay out of my way, Mus’ules!” Wirez snaps backs.

“What’d you say to me, vermin,” the giant growls and stomps up to the smaller alien. Wirez starts firing at him, but Peccs walks into the lasers like they’re just rain. Wirez presses a button on his belt, activating a jetpack that carries him out of the range of Peccs long arms.

With those two occupied, Gaz just has to worry about Tav and Olap. The two stay focused on her. Olap’s speed and agility is tough to keep up with as he matches all of Gaz’s movements.

He crouches on all six limbs and lunges for her like a missile. Gaz barely has time to bring up her war hammer to block, and he tackles her to the ground.

“Gaz!” Dib cries.

Olap lies on top of her, pinning her to the ground with his bottom four arms while his top two are held back by her hammer’s handle. Olap snarls at her, his face only inches from her. Then suddenly, she opens her mouth and bites his nose.

“Oh!” her friends exclaim.

Olap cries out in pain as her teeth digs in his flesh and tries to scurry back. Gaz lets him go, spits out yellow blood, and swings her hammer. She smashes him square in the chest and sends him tumbling across the arena.

Wiping her mouth, Gaz stands up and glares at Tav. He glares back and starts firing his lasers again. She runs for him, sidestepping each laser, and throws her hammer. It spins through the air right for him. He stops firing so he can use his spider legs to block the heavy weapon. As it falls, he sees Gaz right in front of him, fist raised.

She swings at him. With not enough time to block, Tav falls to his knees to dodge and her arm flies over his head.

His spider legs lunge at her. Gaz quickly kicks up her hammer and uses it to block and knocks the appendages off course, but they still slice the sides of her arms. She winces but doesn’t back off.

She swings her leg, kicking Tav in the chest and sending him flying back. His spider legs quickly catch him, digging into the ground, and throw him back. Gaz lifts her hammer, ready to swing, as Tav’s spider legs lunge at her.

The appendages slice across her chest as her hammer smashes into him and sends him crashing into the wall.

Gaz pants, leaning against her hammer as blood drips from her fresh wounds. She doesn’t have long to relax, however, as a large shadow looms over her. She looks back as Peccs swings down at her. She narrowly dodges by leaping out of the way.

“Nice job taking down that Irken,” he says, “and the Swif too. Now it’s just you and me.”

Gaz looks over to where Wirez is lying unconscious on the ground. At some point, Peccs managed to jump up to him flying in the sky and send him crashing back down.

She snarls and swings her hammer into Peccs’ chest. It’s a dead-hit, but he doesn’t even flinch.

“Heh, nice try,” he chuckles, “but we Mus’ules are practically indestructible.”

“Huh,” Gaz grunts, slowly backing away.

“Don’t worry, I won’t break you,” he says, “well, maybe just a little.”

“You talk too much,” Gaz groans.

He swings at her and she skips backwards to dodge. She’s a lot slower and clumsier than before, his large fists almost grazing her. She keeps moving until she backs into the wall.

“End of the line, little one!” Peccs exclaims and swings at her. Gaz leaps to the right to dodge and he smashes the wall.

Before he has a chance to move, Gaz skids around to his back and jabs the end of her hammer’s handle into the back of his knees, causing him to lose his balance and fall against the hall. Then she scrambles onto his back and pulls back her hammer.

“Don’t worry, I won’t break you,” she says.

She smashes her hammer into the back of Peccs’ head, slamming his face into the wall. He twitches before going limp.

Gaz slips off Peccs’ back and takes a look around the arena. Tav, Olap, and Wirez are also lying around, unconscious.

“We have a winner! The last one standing is Gaz of Earth!”

“Yeah!” her team cheers on their balcony, waving and jumping up and down. Soon, the rest of the audience joins in.

“That’s the second win in a row for Earth, putting them officially in first place with ten points!”

Gaz grins and victoriously holds her hammer high.