#EDW to Data Lake

[etl-elt-modernization-migration]

Managed Data Migration: Which businesses should consider

As organizations handle increasing versions of structured and unnecessary data, managed data has become a key task for achieving effective data management. Whether transferred from EDW to data lake or reinforcing the computer system for better analysis, companies should weigh more factors before continuing with ETL/ELT modernization and migration.

Understanding the need for migration Legacy system can slow down commercial operations due to high maintenance costs, scalability issues and limited integration capabilities. ETL migrations and ELT modernization enable businesses to handle large datasets more efficiently, supporting businesses near real-time analytics.

Modernizing your data architecture also involves transition to flexible storage environment such as data lakes, which are ideal for handling various data types. This change supports future AI, ML and BI capabilities by enabling better data access and advanced processing.

Important ideas before starting migration Before starting a managed data project, companies should consider the following:

Data Inventory: Identify and list current data sources to avoid repetition and ensure relevance. Compliance readiness: Compliance with data security should be maintained through the migration process. Adaptation of business goals: Make sure the new environment supports organizational goals as faster insights or cost savings. Workload assessment: Choose between batch treatment or data flow in real time depending on operating needs.

A clearly defined strategy will prevent common pitfalls such as loss of data, downtime or inconsistent reporting.

Choosing the Right Migration Path There are two widely adopted approaches to data movement: ETL Migration: Extract, Transform, Load processes are better for complex transformations before data reaches its destination. ELT Modernization: Extract, Load, Transform allows the target system to handle transformations, offering faster ingestion and scalability.

Role of Data Integration Services A successful migration demands expert handling of source and target compatibility. These services also support data pipeline automation, which improves processing speed and reduces errors from repetitive tasks.

Automated pipelines enable continuous data flow between legacy systems and modern platforms, allowing incremental testing and validation during the process.

Safety and compliance measures Migration opens several access points, increase in contact with data breech. Businesses have to be implemented:

Role-based access control.

End-to-end encryption.

Compliance checks formed with industry standards like GDPR or Hipaa.

Monitoring tools can further help track migration progress and give flags to discrepancies in real time.

Partner with Celebal Technologies In Celebal Technologies, we offer special ETL/ELT modernization and migration solutions for enterprise scalability. From EDW to Data Lake migration, to data pipeline automation and data security compliance, our expert-led approaches ensure a smooth transition with minimal risk. Choose the Celebal Technologies as your partner in management of mass migration with efficiency, accuracy and accuracy.

[teklink]

How to Ensure Data Quality and Governance in Your Enterprise Data Lake

In today’s data-driven world, enterprises are continually collecting massive volumes of data from multiple sources to drive decision-making, enhance customer experiences, and stay competitive. An Enterprise Data Lake serves as a central repository for all this raw data, allowing organizations to store data of all types, structured or unstructured, at any scale. However, without Data Governance and Data Quality measures in place, the effectiveness of an Enterprise Data Lake can quickly diminish, leading to inconsistent data, compliance risks, and poor decision-making.

For more information, visit Teklink International LLC

[techmindsacademy]

7 Best Data Warehouse Tools to Explore in 2025

What is a Data Warehouse?

A data warehouse is a centralized repository designed to store large volumes of data from various sources in an organized, structured format. It facilitates efficient querying, analysis, and reporting of data, serving as a vital component for business intelligence and analytics.

Types of Data Warehouses

Data warehouses can be classified into the following categories:

Enterprise Data Warehouse (EDW): A unified storage hub for all enterprise data.

Operational Data Store (ODS): Stores frequently updated, real-time data.

Online Analytical Processing (OLAP): Designed for complex analytical queries on large datasets.

Data Mart: A focused subset of a data warehouse for specific departments or business units.

Why Use Data Warehouses?

The primary purpose of data warehouses is to store and organize data centrally, enabling faster and more efficient analysis of large datasets. Other benefits include:

Improved Data Quality: Processes ensure data integrity and consistency.

Historical Data Storage: Supports trend analysis and forecasting.

Enhanced Accessibility: Allows seamless access and querying of data from multiple sources.

Who Uses Data Warehouses?

Data warehouses cater to various professionals across industries:

Data Analysts: Query and analyze data for actionable insights.

Data Engineers: Build and maintain the underlying infrastructure.

Business Intelligence Analysts: Generate reports and visualizations for stakeholders.

Analytics Engineers: Optimize data pipelines for efficient loading.

Companies often use data warehouses to store vast amounts of customer data, sales information, and financial records. Modern trends include adopting data lakes and data lake houses for advanced analytics.

Top Data Warehouse Tools to Watch in 2025

1. Snowflake

Snowflake is a cloud-native data warehouse renowned for its flexibility, security, and scalability.

Key Features:

Multi-cluster Architecture: Supports scalability and separates compute from storage.

Virtual Warehouses: On-demand setup for parallel workload handling.

Data Sharing: Facilitates secure data sharing across organizations.

Snowflake integrates seamlessly with tools like dbt, Tableau, and Looker, making it a cornerstone of the modern data stack.

2. Amazon S3

Amazon S3 is a highly scalable, object-based storage service, widely used as a data warehousing solution.

Key Features:

Scalability: Capable of handling any data volume.

AWS Ecosystem Integrations: Enhances processing and analytics workflows.

Cost-effectiveness: Pay-as-you-go pricing model.

Ideal for organizations already leveraging AWS services, Amazon S3 offers unparalleled flexibility and durability.

3. Google Big Query

Google Big Query is a server less, highly scalable solution designed for real-time insights.

Key Features:

Fast Querying: Processes petabytes of data in seconds.

Automatic Scaling: No manual resource management required.

Integrated Machine Learning: Supports advanced analytics.

Big Query’s seamless integration with Google Cloud services and third-party tools makes it a top choice for modern data stacks.

4. Data bricks

Data bricks is a unified analytics platform combining data engineering, science, and business intelligence.

Key Features:

Spark-based Engine: Enables fast, large-scale data processing.

ML flow: Streamlines machine learning lifecycle management.

Real-time Analytics: Processes streaming data effortlessly.

Data bricks supports Python, SQL, R, and Scala, appealing to diverse data professionals.

5. Amazon Redshift

Amazon Redshift is a fully managed, high-performance data warehouse tailored for structured and semi-structured data.

Key Features:

Columnar Storage: Optimized query performance.

Massively Parallel Processing (MPP): Accelerates complex queries.

AWS Integrations: Works well with S3, DynamoDB, and Elastic MapReduce.

Its scalability and cost-effectiveness make it popular among startups and enterprises alike.

6. Oracle Autonomous Data Warehouse

Oracle Autonomous Data Warehouse automates the creation and management of data warehouses using machine learning.

Key Features:

Autonomous Operations: Self-tuning and optimized storage.

Elastic Scalability: Adjusts resources dynamically based on workload.

Built-in ML Algorithms: Facilitates advanced analytics.

Best suited for enterprises seeking robust, automated solutions with high performance.

7. PostgreSQL

PostgreSQL is a versatile, open-source relational database that supports data warehousing needs.

Key Features:

ACID Compliance: Ensures data integrity.

Multi-version Concurrency Control (MVCC): Allows simultaneous access.

Extensibility: Offers plugins like PostgreSQL Data Warehousing by Citus.

Its robust community support and adaptability make PostgreSQL a reliable choice for organizations of all sizes.

Next Steps

Key Takeaways:

Data warehouses enable efficient organization and analysis of large datasets.

Popular tools include Snowflake, Amazon S3, Google BigQuery, Databricks, Amazon Redshift, Oracle, and PostgreSQL.

How to Advance Your Knowledge:

Explore Data Analytics Tools: Get acquainted with platforms like Tableau and dbt.

Learn Data Analytics: Try Career Foundry’s free, 5-day data analytics short course.

Join Live Events: Participate in online events with industry experts.

Take the first step towards becoming a data analyst. Enroll in Career Foundry’s data analytics program and unlock a new career path today.

[hanasatoblogs]

Understanding EDW Modernization in Modern Business

What is EDW Modernization?

EDW, or Enterprise Data Warehouse, is a centralized repository for an organization's structured data. EDW modernization is the process of upgrading and optimizing these data warehouses to meet the evolving needs of modern businesses. This involves leveraging advanced technologies and strategies to enhance performance, scalability, and accessibility of data.

Why is EDW Modernization Crucial?

As businesses become increasingly data-driven, the need for efficient and effective data management becomes paramount. EDW modernization addresses several key challenges:

Data Volume and Velocity: The exponential growth of data necessitates scalable solutions to handle increasing volumes and faster processing speeds.

Data Variety: Modern businesses deal with diverse data types, including structured, semi-structured, and unstructured data. EDW modernization enables the integration and analysis of these varied data sources.

Real-Time Insights: Timely access to accurate data is crucial for making informed business decisions. EDW modernization supports real-time analytics and reporting.

Data Security and Compliance: Protecting sensitive data and ensuring compliance with regulations is a top priority. Modernized EDWs incorporate robust security measures and data governance practices.

Key Strategies for EDW Modernization

Cloud Migration:

Benefits: Enhanced scalability, cost-effectiveness, and reduced infrastructure management overhead.

Considerations: Data security, latency, and potential vendor lock-in.

Data Lake Integration:

Benefits: Unified storage for structured and unstructured data, enabling advanced analytics.

Considerations: Data governance, security, and performance optimization.

Data Virtualization:

Benefits: Real-time access to data from multiple sources without physical data movement.

Considerations: Performance impact and potential complexity.

Advanced Analytics:

Benefits: Deeper insights, predictive analytics, and machine learning capabilities.

Considerations: Data quality, skillset requirements, and model interpretability.

Data Governance and Security:

Benefits: Ensures data accuracy, consistency, and compliance.

Considerations: Ongoing maintenance and enforcement.

The Future of EDW Modernization

As technology continues to evolve, the future of EDW modernization holds exciting possibilities:

AI and Machine Learning: Leveraging AI and ML to automate data ingestion, cleaning, and analysis.

Real-Time Streaming Analytics: Processing and analyzing data as it is generated for instant insights.

Edge Computing: Analyzing data closer to the source for faster decision-making.

Quantum Computing: Solving complex data problems that are intractable for classical computers.

By embracing EDW modernization, organizations can unlock the full potential of their data, drive innovation, and gain a competitive edge in the digital age.

[alookathersoul]

Data Lakes Reinvented New age Applications

The data lake solutions has earned a wide reputation in the latest years. It boasts of the modern design pattern which is capable of fitting the data of the latest time. This pattern is useful to the potential audience in using and organizing the data. 

For instance, a wide array of business enterprises intends to incorporate the data faster into the lake. Hence, employees of the organization can avail the data for analytics and operation. 

The organization aims to store the data in the raw and original state, which offers a helping hand in processing it in various layers. It brings a revolution in the operations of business analytics. They want to capture the unstructured data, big data and other data from different sources.

The potential audience is under tremendous pressure for creating an organizational advantage and business value from different types of data collections through different discovery-oriented analytics. 

A data lake provides assistance with all the needs and trends as the potential audience can resolve all the challenges of the data lake. As data lake is totally new, the design patterns and best practices are still a bit baffling. 

Here are some of the primary applications of a data lake which are beneficial to the data management professionals along with the business counterparts.

Incorporation of data faster with no or less front improvement

Early ingestion and late processing happen to be an integral part of the data lake. As you opt for the late processing and early ingestion practice, it offers the suitable choice to integrate to the data for the reporting, operations, and analytics. 

It demands different diverse ingestion techniques for handling diverse interfaces, data structures, and container types which are useful in scaling to real-time latencies and massive data volume. It is also useful in simplification of the onboarding of data sets and data sources.

Persisting the data into the raw state for preserving the original schema and details

Detailed source data is known to be preserved into the storage. So, you will be capable of repurposing the data continuously with the upcoming of new business needs. In addition to this, raw data is necessary for discovery-oriented analytics and exploration. This data works wonders with detailed data, large data sample, and data anomalies.

Controller the loading of data into the lake

If the data is not controlled, there are risks that it might get converted to the data swamp. Due to this, the data lake becomes an undocumented and disorganized data set which cannot be leveraged, governed and navigated easily. You can make the best use of policy-based data governance for seeking control. The data curator and steward enforce the anti-dumping policies into the data lake.

Also, the policies provide exceptions as the data scientist, and data analyst throws the data in the analytics sandboxes. You need to document the data, once it goes into the lake with the aid of the business glossary, information catalogue, metadata, and different semantics. It provides the choice to the potential audience for optimizing queries, finding the data, governing the data. It also plays an integral role in decreasing data redundancy.

Integration of data into the structure, diverse sources and vintages 

A wide array of the potential audience makes use of the modern big data and traditional enterprise data on the Hadoop-based data to increase the customer views, advanced analytics, enriching different cross-source correlations to seek insightful segments and clusters. Besides this, some business organization seeks the blended data lake to allow sentiment analysis, logistics optimization, predictive maintenance, to name a few.

Capturing big data and different new data sources within the data lake

An integral part of data lakes are deployed with the aid of Hadoop, whereas few of them are deployed on traditionally systems and Hadoop partially. There are a plethora of data lakes which handle big data since Hadoop contributes to being a perfect choice. Hadoop-based data lakes offer a helping hand in capturing massive data catalogue from a bunch of new sources.

Different architectural and technical purposes

A single data lake will be capable of fulfilling different architectural objectives which include data staging and landing. As Data Lake comes with a variety of architectural roles, it should be distributed in a bunch of data platforms, where each of them come with unique processing and storage features.

Improving and extending the new and old data architecture

A wide part of the data lakes are considered to be an integral part of the multiplatform data ecosystem. Common instances of such data lakes include omni-channel marketing, data warehouse environment, and digital supply chain. Also, different traditional applications of a data lake are inclusive of content management, financial, multi-module ERP, document archiving. The data lake is recognized to be the modernization strategy which is useful in extending the functionality of the data environment.

Choosing different data management platforms which accomplish data lake needs

Hadoop is considered to be the preferred data platform owing to the linear scalability, lower price and powerful for analytics. But, few potential audiences implement MPP or massive parallel processing relational database as the data lake needs relational processing.

Allowing self-service best practices

It is inclusive of data visualization, data preparation, data exploration and different types of analytics. A wide array of the savvy potential audience wants access to the data lake. Key components allow self-service functionality.

Hybrid platforms have become the latest buzz in the town. This data storage platform is beneficial in analyzing, processing and holding the unstructured and structured data. It is possible to use such a platform in combination with EDW and enterprise data warehouse. 

Such a data storage platform helps in saving an ample amount of money as business enterprises make use of easy to obtain and budget-friendly hardware. In the data lake, data are preloaded in the raw formats. Instead, they are preconfigured after an entry into the company systems. 

They are considered to be the combination of relational and Hadoop systems on the on-cloud and on-premises systems. With a wide array of data collections, the company finds a rise in cloud storage.

[crazieh]

Myths about data lakes and their role in enterprise data storage

Data Lake is the latest trend in the market. There are certain misconceptions and myths which are proliferated across the community of data management. To gain more insights, it is necessary to gain information about the myths about Data lakes. In the beginning, you need to define what a data lake is so that you can understand that everyone is on the same page.

A data lake solutions is the user-defined method that helps to organize diverse and large volumes of data. The data lake can be used on different data management platforms, like relational databases, Hadoop clusters, clouds, relationship databases, etc. Based on the platform, the data lake can handle a variety of data types, including structured data, semi-structured data, and unstructured data.

For most business enterprises, the data lake offers support to several use cases, including data warehouse extensions, advanced analytics, broad data exploration, data staging, and data landing. Data lakes are beneficial in different departments such as supply chain and marketing and various industries like logistics and healthcare.

Here are some of the myths associated with data lakes.

Data Lakes are useful for Internet organizations only

Internet firms were the pioneer of data lake and Hadoop. We will always be thankful to them for bringing such massive innovations to the industry. However, there are several other companies that have come up with data lakes in the production in different mainstream industries, like insurance, finance, healthcare, pharma, and telco.

Few data lakes serve various departmental analytics and operations. Other organizations have come up with several analytic forms that operate on the Data lake, which are inclusive of clustering, text and data mining, predictive analytics, graph, and natural language processing. It would be best if you keep in mind that data lake-based analytics supports a variety of applications like customer segmentation, risk calculations, security breaches, fraud detection, and insider trading, to name a few.

Data Lake is a dumping ground

At times, the database might turn into a dumping ground. However, early adopters do not treat the data lake as a mere dumping ground. Instead, the data lake is treated as the balancing act. However, few of the customers dump the data, whereas many of them do not. Data scientists, Data analysts, and power users should create data sandboxes in work. They can take the data out and into the lake freely, till they can govern themselves. But the majority of other users need to petition the lake curator, or steward, who will vet the incoming data.

Hadoop is a for Data Lake

The latest survey has revealed that more than half of data lakes involved in production are exclusively on Hadoop. But Hadoop is not a must-have for data lakes. Few of the data lakes are on the relational database management systems. It would be best if you keep in mind that data lake is not like any other logical data architecture, which is distributed physically across several platforms.

It explains why a certain part of data lakes are deployed on top of the Hadoop cluster, which is known to be integrated with any RDBMS. There are chances that each one of them will turn into a cloud.

Lake Data is a product that can be purchased

Data Lake is the reference architecture that is not dependent on technology. It happens to be an approach used by business organizations to use data as the focal point of business operations. It is inclusive of quality, governance, and data management, which allows self-service analytics to provide empowerment to data customers. It would be best to remember that the data lake is not any other product, which can be purchased. It is not possible to purchase a data warehouse product and refer to it as the data lake.

Customers will come only if we create Data Lakes

Implementation of Data Lake does not necessarily indicate that technical and business users will flock into it automatically. They will not come till there is a compelling business case. Business users need to perform data preparation, data exploration, and visualization with the aid of Data Lake. Instead, they want the data in the self-service fashion. Also, the potential audience will not be able to stay if you offer trusted, supreme quality, and governed data. Also, business users will not be successful without any certain training and consultants.

All the Data Lakes get converted into data swamps.

There is no doubt that the data lake might get converted into the data swamp. It is recognized to be a disorganized and undocumented data store, and trusting, using, and navigating the data store can be challenging. Data swamp results, owing to the absence of data governance, curation, stewardship, lack of control over the incoming data, and access to the data lake.

A data lake can be a replacement for the data warehouse

The data lake is known to incorporate several data warehouses along with different data sources. All of them come from the data lake in which the governance will be embedded, simplification of trusted data discovery for the users across the business organization.

The data lake will augment different EDW environments that provide the suitable choice to enable and empower data analysts and data scientists to easily explore the data. It also provides a helping hand in discovering new insights and new perspectives. Besides this, it is useful in boosting business growth and accelerating innovation.

Summary

Thanks to the Internet of Things, applications, and smart devices, the amount of unstructured data will grow exponentially. So, the demand for storing the data will intensify. With the adoption data lake, there will be an increase in the majority of the organization across the globe. If you want to avoid a data swamp, the data steward needs to curate the lake data, whereas the governance policies should be capable of defining the standards and controls for the lake and the data.

[ahanapearl]

How to evaluate enterprise data warehouses?

The best way to evaluate enterprise data warehouses is by examining your business needs. For a better perspective, we recommend some general EDW data integrations for every enterprise.

Self-service analytics software-

This is for making decisions that are based on relevant reports and can be customized according to the enterprise’s needs.

Machine learning (ML) software-

This is specifically for training ML models on advanced analytics and structured data. In case you want to explore more on Machine Learning, read how ML helps in detecting financial fraud.

Data lake software-

This is for storing semi-structured, structured, and unstructured data. Typically, data lake is a single store of data that contains raw layouts of sensor data, system data and social data. You can establish this software with an on premise EDW or a cloud-based EDW.

The true business benefit of having a enterprise data warehouse is that it automatically increases competitive strategy and helps you level up in every aspect. With an enterprise software development company, you can guard your data in any market.

Lear more on Enterprise Data Warehousing here.

[forcebolt1123-blog]

Databricks vs Snowflake – An Interesting Evaluation

When a full stack development talks about the world being substantially influenced by data infrastructure, two cutting-edge data technologies are frequently mentioned – Snowflake and Databricks. They represent two data-dependent areas with a modern twist and enable cloud architecture via Azure, Google Cloud, and AWS.

The implementation of Data Lake and Enterprise Data Warehouse (EDW) was the starting point. Over time, Snowflake developed a modernized version of EDW, and Databricks developed an upgraded version of Data Lake.

Today, an interesting comparison is being made between Databricks and Snowflake, which shows some parallels but with distinct qualities of their own. However, before we compare them, let’s take a closer look at each of them.

[updatesnews]

How to choose a cloud data warehouse

How to choose a cloud data warehouse

Enterprise data warehouses, or EDWs, are unified databases for all historical data across an enterprise, optimized for analytics. These days, organizations implementing data warehouses often consider creating the data warehouse in the cloud rather than on premises. Many also consider using data lakes that support queries instead of traditional data warehouses. A third question is whether you want…

[lucasrfsblog]

Arquitetura Datalake: Extraindo Valor dos Dados – Estudo de caso

Estudo de caso

Definição de arquitetura de um data lake em um cenário real

Você é um consultor de engenharia de dados atribuídos a um projeto de um dos clientes. O cliente precisa criar um repositório de dados que pode ajudar a empresa a tomar decisões baseadas em dados. Então equipe criou alguns painéis e relatórios iniciais para o Departamento Financeiro, e posteriormente outros departamentos, como RH, Marketing, Vendas e Operações também começam a exigir o mesmo serviço, conforme se verifica nas imagens a seguir.

                          Fonte: https://excalidraw.com/#json=5297386997940224,VMfm2r799z0qBBckbE0g5Q

 [Questões]

 Após sua primeira semana no cliente, você faz as seguintes observações:

1. A ferramenta DataViz é conectada diretamente às fontes de dados.

2. Os usuários reclamaram com a equipe de suporte de TI de que alguns sistemas diminuíram o desempenho.

3. A cada dois dias, os painéis e relatórios são quebrados e a equipe de dados tempo para identfcar o que aconteceu.

4. Arquitetura proposta e ferramentas de dados.

.

Após a primeira semana, você faz as seguintes observações:

/ A ferramenta Data Viz é conectada diretamente às fontes de dados.

/ Os usuários reclamaram para a equipe de Suporte de TI que alguns sistemas diminuíram o desempenho.

/ A cada dois dias, os painéis e relatórios são quebrados e a equipe de dados não tem tempo para identificar o que aconteceu.

Relação com o tema da disciplina

Escolha da melhor metodologia, frameworks e ferramentas de data lake.

Ferramentas e métodos:

// ACESSE AQUI

Dada a sua experiência como estudante da disciplina de Arquitetura de data lake e com relação aos assuntos abordados, compartilhe suas ideias sobre a infraestrutura atual. Quais modificações você sugeriria, se houver?

 1-    A primeira ação que iria sugerir é tirar o custo de processamento de dados ou transformação dos dados da ferramenta de dashboard ou relatórios.

2-    Criar uma estrutura de dados intermediaria para armazenar os dados. (Data lake em Cloud como sugestão)

3-    Plugar ferramental de ETL disponíveis no próprio cloud para trabalhar este dado para catalogar e indexar essa massa de dados.

4-    Se necessário consumir licenciamento de exibição dos dados no próprio cloud ou optar pelo consumo dos dados já trabalhados de forma on-premisse.

 Orquestrador: Airflow.

 Lambda:

   Hot Path: Streaming

Cold Path: Batch execution

  Kappa:

Link: https://docs.microsoft.com/pt-br/azure/architecture/data-guide/big-data/

 [AWS Datalake]

 Fonte: https://aws-quickstart.github.io/quickstart-datalake-foundation/#_quick_start_reference_deployments

  [Toyota Datalake]

 Fonte: https://aws.amazon.com/pt/blogs/big-data/enhancing-customer-safety-by-leveraging-the-scalable-secure-and-cost-optimized-toyota-connected-data-lake/

 [Azure Data analytics]

Fonte: https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/advanced-analytics-on-big-data

 [Azure data explorer]

  Fonte: https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/big-data-azure-data-explorer

  Fonte: https://docs.microsoft.com/en-us/azure/architecture/solution-ideas/articles/cloud-scale-analytics-with-discovery-hub

  [GCP Datalake]

https://cloud.google.com/architecture/build-a-data-lake-on-gcp

  https://dev.to/giulianobr/data-lake-on-google-cloud-platform-1jf2

  [Modern data stack]

  https://dev.to/giulianobr/data-lake-on-google-cloud-platform-1jf2

Fonte: https://continual.ai/post/the-modern-data-stack-ecosystem-fall-2021-edition

[Arquitetura Modelo]

 [DBZIUM]

 Change data capture:  https://debezium.io/

 Debezium is an open source distributed platform for change data capture. Start it up, point it at your databases, and your apps can start responding to all of the inserts, updates, and deletes that other apps commit to your databases. Debezium is durable and fast, so your apps can respond quickly and never miss an event, even when things go wrong

 Data Parquet.  (Apache)

 [Extra]

Comparação entra ferramentas de EDW (Snowflake, Redshift, Bigquery, Firebolt)

https://hevodata.com/blog/snowflake-vs-redshift/ https://poplindata.com/data-warehouses/2021-database-showdown-bigquery-vs-redshift-vs-snowflake/ https://www.firebolt.io/comparison/snowflake-vs-bigquery https://www.firebolt.io/blog/snowflake-vs-redshift-vs-firebolt https://www.geeksforgeeks.org/google-big-query-vs-redshift-vs-snowflakes/

Hands on de como deployar o Debezium+Kafka coletando os dados do postgresql

https://medium.com/@tilakpatidar/streaming-data-from-postgresql-to-kafka-using-debezium-a14a2644906d

Uma introdução bem interativa sobre a ferramenta de CDC Debezium https://medium.com/event-driven-utopia/a-visual-introduction-to-debezium-32563e23c6b8

Data observability ou Observabilidade de pipelines/dados Hands on https://lopesdiego12.medium.com/monitoramento-de-data-pipelines-grafana-apache-airflow-d606740afff

Como criar o primeiro pipeline no Airflow Hands on https://lopesdiego12.medium.com/como-criar-seu-primeiro-data-pipeline-no-apache-airflow-dbe3791a4053

[appdevelopmentcompanyinusa]

Consagous is one of the biggest big data analytics companies provide big data analytics services for better business decisions. We offer EDW optimization, data lake, stream analytics, data modeling and algorithm development implementation services. Reach out us to get the best big data analytics solutions from experts. https://bit.ly/2DxCp4G

[martechadvisor-blog]

The Data You Need for Effective Personalization

Kabir Shahani, CEO & Co-founder of Amperity, outlines in detail what companies need to do to harness the power of their customer data Take a look at the latest MarTech Infographic. There are logos from 6,242 vendors trying to help brands personalize their websites, target audiences more efficiently, and optimize every distinct touchpoint and interaction. Personalization technologies have never been more advanced.

And consumers have never been more receptive. Last year Accenture reported that 41% of consumers switched brands last year due to a lack of personalization and trust. Our analysts here at Boston Consulting Group predict a massive $800 billion revenue shift, over the next 5 years, to the 15% of brands that get personalization right. Consumers want personalization.

So why isn’t every brand jumping on the personalization train and riding off into their big piles of cash? Because their customer data isn’t unified or usable.

Data - from email tools, clickstream, in-store transactions, and loyalty databases to CRMs, MDMs, EDWs, DMPs, analytics and BI tools - is trapped and siloed. Without a complete picture of all your customers’ purchases, preferences, and identities, effective personalization is impossible.

Do you really need all of your customer data, intelligently unified, to get personalization right? Yes, you do. How many times have your ads following your customers around the internet, after they’ve already purchased the product, wasting ad dollars and annoying customers? How often do you send the exact same email to all four of a customer’s email addresses, treating them like 4 separate people and urging them to unsubscribe? Is your website personalized to your customers’ actual tastes, not just the last product they happened to browse, optimizing it for conversions and revenue? If the answer is ‘no’ to any of these questions, you have a customer data problem -- one that’s costing you money, customer loyalty, and long-term growth.

And while the world’s most loved brands are struggling, Internet-only brands, whose customer data capabilities were built for personalization, are redefining what’s possible. Will you be in the 15% of brands that get personalization right? If you want to be, the path forward is to rethink and rebuild your customer data management capabilities from the ground up.

Building Customer Data Intelligence Capabilities that Work

There are three key steps to making your disparate customer data used for personalization. First, you need it all in one, central place. Then you need to resolve customer identities to form individual customer profiles. Finally, you need to take action on the data by using it in all your customer touchpoints.

Step One: Co-locate Your Data

The first step is to bring all your customer data in a centralized data store. At this point, you just want it co-located together. Many teams make the mistake of using an EDW or a CRM tool, which has a hard-coded schema and requires months of data transformations to get data in. This approach is rigid and breaks whenever you want to add or change data sources, which is a huge waste of your team’s time and your marketing budget, ultimately leaving you without access to the data you need, when you need it.

Instead, co-locate your raw data in a flexible, scalable data store without a predefined schema (think data lake). This means no lengthy transformations and fast and easy setup. With this approach, new sources can be easily integrated at any time, future-proofing your investments and giving you the freedom to always choose best-of-breed technologies as they become available.

The data store must also be scalable because data from all your systems can quickly add up to billions of records. A speed of ingestion also matters, which is again why a scalable platform and raw data ingestion are keys to being able to use your data while it is still current.

Step Two: Connect Your Data

Once you’ve brought all your data into a flexible, scalable, and centralized data store, you need to unify it into rich, cross-source customer profiles.

In a perfect world, all of your datasets would share a unifying key (like a social security number or a thumbprint). Using a simple join, you could connect all your data and build profiles. In the real world, however, the majority of your data sources don’t have these keys. Some brands try to solve for this using static business rules, but this approach is lossy, inaccurate, and hard to maintain as data sources change over time.

Instead, top brands do like the Googles and the Amazons do. They use machine learning.

Machine learning algorithms, which have been trained on massive amounts of customer data, scour your records for matches in a process called intelligent identity resolution. Clusters of records across datasets are then accurately linked, based on the unique features of the data, not the best guesses of your team. This approach results in huge lifts in the completeness of profiles. And the more complete your profiles are, the better your personalization becomes.

Step Three: Syndicate Your Data

Finally, you need to fuel all your various systems of engagement (email, social, site, etc) with rich, unified customer data. This requires integrations with all your external systems and a central data store that never traps your data. Like when data was brought in, it must now be syndicated out using integrations built for speed and scale.

The last comment on using data for personalization: personalization requires knowing customers intimately based on all the data they share with you. As you begin to experiment with new personalization initiatives, you will enrich your understanding of customers, based on how customers respond to your efforts. This data should also be circulated back into your customer data intelligence capabilities for iterative improvements and optimization over time.

In summary, some important questions to consider:

Can you easily bring all your disparate customer data into a flexible and centralized data store?

Can you accurately resolve customer identities across all your data, even when your systems lack unifying keys?

And last, can you drive meaningful personalization to use your existing systems of engagement powered by rich and unified customer profiles?  

If the answer to any of these questions is ‘no’, it’s time to invest in your customer data intelligence capabilities. Many brands are pursuing unified and usable customer data by some means, but most have yet to find success. With a system built from the ground up for scale, accuracy, and completeness, you can ensure that yours in the 15% of brands that get personalization right.

This article was first appeared on MarTech Advisor

[megatechcrunch]

Comcast’s system of storing schemas and metadata enables data scientists to find, understand, and join data of interest.

In the olden days of data science, one of the rallying cries was the democratization of data. No longer were data owners at the mercy of enterprise data warehouses (EDWs) and extract, transform, load (ETL) jobs, where data had to be transformed into a specific schema (“schema on write”) before it could be stored in the enterprise data warehouse and made available for use in reporting and analytics. This data was often most naturally expressed as nested structures (e.g., a base record with two array-typed attributes), but warehouses were usually based on the relational model. Thus, the data needed to be pulled apart and “normalized" into flat relational tables in first normal form. Once stored in the warehouse, recovering the data’s natural structure required several expensive relational joins. Or, for the most common or business-critical applications, the data was “de-normalized,” in which formerly nested structures were reunited, but in a flat relational form with a lot of redundancy.

This is the context in which big data and the data lake arose. No single schema was imposed. Anyone could store their data in the data lake, in any structure (or no consistent structure). Naturally nested data was no longer stripped apart into artificially flat structures. Data owners no longer had to wait for the IT department to write ETL jobs before they could access and query their data. In place of the tyranny of schema on write, schema on read was born. Users could store their data in any schema, which would be discovered at the time of reading the data. Data storage was no longer the exclusive provenance of the DBAs and the IT departments. Data from multiple previously siloed teams could be stored in the same repository.

Where are we today? Data lakes have ballooned. The same data, and aggregations of the same data, are often present redundantly—often many times redundant, as the same interesting data set is saved to the data lake by multiple teams, unknown to each other. Further, data scientists seeking to integrate data from multiple silos are unable to identify where the data resides in the lake. Once found, diverse data sets are very hard to integrate, since the data typically contains no documentation on the semantics of its attributes. Attributes on which data sets would be joined (e.g., customer billing ID) have been given different names by different teams. The rule of thumb is that data scientists spend 70% of their time finding, interpreting, and cleaning data, and only 30% actually analyzing it. Schema on read offers no help in these tasks, because data gives up none of its secrets until actually read, and even when read has no documentation beyond attribute names, which may be inscrutable, vacuous, or even misleading.

Enter data governance. Traditionally, data governance is much more akin to EDWs than data lakes—formal management and definition, controlled vocabularies, access control, standardization, regulatory compliance, expiration policies. In the terms of a recent Harvard Business Review article, “What’s Your Data Strategy?”, by Leandro DalleMule and Thomas H. Davenport, (traditional) data governance does a good job at the important reactive (“defensive”) elements of data management—“identifying, standardizing, and governing authoritative data sources, such as fundamental customer and supplier information or sales data, in a 'single source of truth'”—but is less well-suited to proactive (“offensive”) efforts. In contrast, proactive strategies “focus on activities that generate customer insights (data analysis and modeling, for example) or integrate disparate customer and market data to support managerial decision-making.”

Today’s data governance retains some of its traditional reactive roots. But increasingly in the big data arena, proactive data governance is saving the democratized data lake from itself.

At Comcast, for instance, Kafka topics are associated with Apache Avro schemas that include non-trivial documentation on every attribute and use common subschemas to capture commonly used data (such as error logs). These schemas follow the data through its streaming journey, often being enriched and transformed, until the data finds its resting place in the data lake. "Schema on read” using Avro files thus includes rich documentation and common structures and naming conventions. More accurately, a data lake of Avro data can be characterized as “schema on write,” with the following distinctions from traditional schema on write: 1) nested structures instead of flat relations; 2) schemas defined by data owners, not DBAs; and 3) multiple schemas not only supported but encouraged. Further, the data includes its own documentation.

At Comcast, we store schemas and metadata on Kafka topics, data lake objects, and connecting/enriching processes in Apache Atlas. Atlas provides data and lineage discovery via sql-like, free-text, and graph queries. Our system thus enables data scientists to find data of interest, understand it (via extensive attribute-level documentation), and join it (via commonly named attributes). In addition, by storing the connecting/enriching processes we provide data lineage. A data producer can answer the question: “Where are the derivatives of my original data, and who transformed them along the way?” A data scientist can answer the question: “How has the data changed in its journey from ingest to where I’m viewing it in the data lake?”

Proactive data governance transforms schema on read to schemas on write, enabling both flexibility and common semantics.

This post is a collaboration between O'Reilly and Qubole. See our statement of editorial independence.

Continue reading Data governance and the death of schema on read.

from All - O'Reilly Media http://ift.tt/2pAz78S

[csemntwinl3x0a1]

Rethinking data marts in the cloud

Rethinking data marts in the cloud

Become more agile with business intelligence and data analytics.

Many of us are all too familiar with the traditional way enterprises operate when it comes to on-premises data warehousing and data marts: the enterprise data warehouse (EDW) is often the center of the universe. Frequently, the EDW is treated a bit like Fort Knox; it's a protected resource, with strict regulations and access rules. This setup translates into lengthy times to get new data sets into an EDW (weeks, if not months) as well as the inability to do exploratory analysis on large data sets because an EDW is an expensive platform and computational processing is shared and prioritized across all users. Friction associated with getting a data sandbox has also resulted in the proliferation of spreadmarts, unmanaged data marts, or other data extracts used for siloed data analysis. The good news is these restrictions can be lifted in the public cloud.

A new set of opportunities for BI in the cloud

Business intelligence (BI) and analytics in the cloud is an area that has gained the attention of many organizations looking to provide a better user experience for their data analysts and engineers. The reason frequently cited for the consideration of BI in the cloud is that it provides flexibility and scalability. Organizations find they have much more agility with analytics in the cloud and can operate at a lower cost point than has been possible with legacy on-premises solutions.

The main technology drivers enabling cloud BI are:

1.The ability to cost-effectively scale data storage in a single repository using cloud storage options such as Amazon S3 or Azure Data Lake Store (ADLS).

2.The ease that one can acquire elastic computational resources of different configurations (CPU, RAM, and so on) to run analytics on data combined with the utility-based cost model where you pay for only what you use. Discounted spot instances can also offer a unique value for some workloads.

3.An open and modular architecture consisting of analytic optimized data formats like Parquet and analytic processing engines such as Impala and Spark, allowing users to access data via SQL, Java, Scala, Python, and R directly and without data movement.

These capabilities make for an amazing one-two-three punch.

Because the cloud offers the ability to decouple storage and compute, all of an organization's data can now live in a single place, thus eliminating data silos, and departments and teams can provision computes to run analytics for their use cases as needed. This new arrangement means self-service BI and analytics are a reality for those who adopt such a model. And with an open architecture, there are no worries about technology lock-ins.

Architecture patterns for the cloud

Now that we've discussed what technology options there are for BI in the cloud, what are the considerations an organization should think about?

Generally speaking, there are two common use cases for BI and analytics in the cloud that map to the two main architecture patterns: long-lived clusters and short-lived (or transient) clusters. Let's discuss each in more detail.

Transient (short-lived) clusters for individuals or small teams

Often, data analysts, data scientists, and data engineers want to investigate new and potentially interesting data sets, but would like to avoid as much friction as possible in doing so. It's quite common for data sets to originate in the cloud, so storing and analyzing them in the cloud is a no-brainer. Such data sets can easily be brought into S3 or ADLS in their raw form as a first step.

Next, a cluster can easily be provisioned with the instance type and configuration of choice, including potentially using spot instances to reduce cost. Generally, instances for transient clusters need only minimal local disk space, since data processing runs directly on the data in the cloud storage. There are tools, like Cloudera Director, that can assist with the instance provisioning and software deployment, making it as easy as a few clicks to provision and launch a cluster. Once the cluster is ready, data exploration can take place, allowing the data analyst to perform an analysis. If a new data set will be created as part of the work, it can be saved back to the cloud storage.

Another advantage to compute-only clusters is that they can easily and quickly be resized, allowing for growth or shrinkage, depending on data processing needs. When the analysis is finished, the cluster can be destroyed.

One of the main benefits of transient clusters is it allows individuals and groups to quickly and easily acquire just-in-time resources for their analysis, leveraging the pay-as-you-go cost model, all while providing resource isolation. Unlike an on-premises deployment in which multiple tenants share a single cluster consisting of both storage and compute and often compete for resources, teams become their own tenants of a single compute cluster, while being able to share access to data in a common cloud storage platform.

Long-lived clusters for large groups and shared access

The other common use case for BI and analytics in the cloud is a shared cluster that consists of many tenants. Unlike the transient cluster that may only run for a few hours, long-lived clusters may need to be available 24/7 to provide access to users across the globe or to data applications that are constantly running queries or accessing data. Like transient clusters, long-lived clusters can be compute-only, accessing data directly from cloud storage, or like on-premises clusters, they can have locally attached storage and HDFS and/or Kudu. Let's discuss the use cases for both.

Long-lived compute-only clusters

For multitenant workloads that can vary in processing requirements over time, long-lived compute-only clusters work best. Because there is no local data storage, compute-only clusters are elastic by definition and can be swiftly resized based on the processing demands. During peak demand, a cluster can be scaled up so that query times meet SLA requirements, and during hours of low demand, the cluster can be scaled down to save on operational costs. This configuration allows the best of both worlds—tenants’ workloads are isolated from each other, as they can be run on different clusters that are tuned and optimized for the given workload. Additionally, a long-lived compute-only cluster consisting of on-demand or reserved instances can be elastically scaled up to handle additional demand using spot instances, providing a very cost-effective way to scale compute.

Long-lived clusters with local storage

While elastic compute-only clusters offer quick and easy scale-up and scale-down because all data is remote, there may be some workloads that demand lower latency data access than cloud storage can provide. For this use case, it makes sense to leverage instance types with local disk and to have a local HDFS available. This cloud deployment pattern looks very similar to on-premises deployments; however, it comes with an added benefit: access to cloud storage. As a result, a cluster can have tables that store the most recent data in partitions locally in HDFS, while older data resides in partitions backed by cloud storage, providing a form of storage tiering.  For example, the most recent month of sales data could reside in partitions backed by local HDFS storage providing the fastest data access, and data older than one month could reside in cloud object storage.

In summary

Deploying data marts in the cloud can help an organization be more agile with BI and data analytics, allowing individuals and teams to provision their own compute resources as needed while leveraging a single, shared data platform. If you're interested in learning more about how to architect analytic workloads, including the core elements of data governance, for the cloud, be sure and attend my talk at Strata Data in Singapore, Rethinking data marts in the cloud: Common architectural patterns for analytics.

Continue reading Rethinking data marts in the cloud.

http://ift.tt/2zxjfI8

[forcebolt1123-blog]

Databricks vs Snowflake – An Interesting Evaluation

When a full stack development talks about the world being substantially influenced by data infrastructure, two cutting-edge data technologies are frequently mentioned – Snowflake and Databricks. They represent two data-dependent areas with a modern twist and enable cloud architecture via Azure, Google Cloud, and AWS.

The implementation of Data Lake and Enterprise Data Warehouse (EDW) was the starting point. Over time, Snowflake developed a modernized version of EDW, and Databricks developed an upgraded version of Data Lake.

[repmywind02199]

Rethinking data marts in the cloud

Rethinking data marts in the cloud

Become more agile with business intelligence and data analytics.

Many of us are all too familiar with the traditional way enterprises operate when it comes to on-premises data warehousing and data marts: the enterprise data warehouse (EDW) is often the center of the universe. Frequently, the EDW is treated a bit like Fort Knox; it's a protected resource, with strict regulations and access rules. This setup translates into lengthy times to get new data sets into an EDW (weeks, if not months) as well as the inability to do exploratory analysis on large data sets because an EDW is an expensive platform and computational processing is shared and prioritized across all users. Friction associated with getting a data sandbox has also resulted in the proliferation of spreadmarts, unmanaged data marts, or other data extracts used for siloed data analysis. The good news is these restrictions can be lifted in the public cloud.

A new set of opportunities for BI in the cloud

Business intelligence (BI) and analytics in the cloud is an area that has gained the attention of many organizations looking to provide a better user experience for their data analysts and engineers. The reason frequently cited for the consideration of BI in the cloud is that it provides flexibility and scalability. Organizations find they have much more agility with analytics in the cloud and can operate at a lower cost point than has been possible with legacy on-premises solutions.

The main technology drivers enabling cloud BI are:

1.The ability to cost-effectively scale data storage in a single repository using cloud storage options such as Amazon S3 or Azure Data Lake Store (ADLS).

2.The ease that one can acquire elastic computational resources of different configurations (CPU, RAM, and so on) to run analytics on data combined with the utility-based cost model where you pay for only what you use. Discounted spot instances can also offer a unique value for some workloads.

3.An open and modular architecture consisting of analytic optimized data formats like Parquet and analytic processing engines such as Impala and Spark, allowing users to access data via SQL, Java, Scala, Python, and R directly and without data movement.

These capabilities make for an amazing one-two-three punch.

Because the cloud offers the ability to decouple storage and compute, all of an organization's data can now live in a single place, thus eliminating data silos, and departments and teams can provision computes to run analytics for their use cases as needed. This new arrangement means self-service BI and analytics are a reality for those who adopt such a model. And with an open architecture, there are no worries about technology lock-ins.

Architecture patterns for the cloud

Now that we've discussed what technology options there are for BI in the cloud, what are the considerations an organization should think about?

Generally speaking, there are two common use cases for BI and analytics in the cloud that map to the two main architecture patterns: long-lived clusters and short-lived (or transient) clusters. Let's discuss each in more detail.

Transient (short-lived) clusters for individuals or small teams

Often, data analysts, data scientists, and data engineers want to investigate new and potentially interesting data sets, but would like to avoid as much friction as possible in doing so. It's quite common for data sets to originate in the cloud, so storing and analyzing them in the cloud is a no-brainer. Such data sets can easily be brought into S3 or ADLS in their raw form as a first step.

Next, a cluster can easily be provisioned with the instance type and configuration of choice, including potentially using spot instances to reduce cost. Generally, instances for transient clusters need only minimal local disk space, since data processing runs directly on the data in the cloud storage. There are tools, like Cloudera Director, that can assist with the instance provisioning and software deployment, making it as easy as a few clicks to provision and launch a cluster. Once the cluster is ready, data exploration can take place, allowing the data analyst to perform an analysis. If a new data set will be created as part of the work, it can be saved back to the cloud storage.

Another advantage to compute-only clusters is that they can easily and quickly be resized, allowing for growth or shrinkage, depending on data processing needs. When the analysis is finished, the cluster can be destroyed.

One of the main benefits of transient clusters is it allows individuals and groups to quickly and easily acquire just-in-time resources for their analysis, leveraging the pay-as-you-go cost model, all while providing resource isolation. Unlike an on-premises deployment in which multiple tenants share a single cluster consisting of both storage and compute and often compete for resources, teams become their own tenants of a single compute cluster, while being able to share access to data in a common cloud storage platform.

Long-lived clusters for large groups and shared access

The other common use case for BI and analytics in the cloud is a shared cluster that consists of many tenants. Unlike the transient cluster that may only run for a few hours, long-lived clusters may need to be available 24/7 to provide access to users across the globe or to data applications that are constantly running queries or accessing data. Like transient clusters, long-lived clusters can be compute-only, accessing data directly from cloud storage, or like on-premises clusters, they can have locally attached storage and HDFS and/or Kudu. Let's discuss the use cases for both.

Long-lived compute-only clusters

For multitenant workloads that can vary in processing requirements over time, long-lived compute-only clusters work best. Because there is no local data storage, compute-only clusters are elastic by definition and can be swiftly resized based on the processing demands. During peak demand, a cluster can be scaled up so that query times meet SLA requirements, and during hours of low demand, the cluster can be scaled down to save on operational costs. This configuration allows the best of both worlds—tenants’ workloads are isolated from each other, as they can be run on different clusters that are tuned and optimized for the given workload. Additionally, a long-lived compute-only cluster consisting of on-demand or reserved instances can be elastically scaled up to handle additional demand using spot instances, providing a very cost-effective way to scale compute.

Long-lived clusters with local storage

While elastic compute-only clusters offer quick and easy scale-up and scale-down because all data is remote, there may be some workloads that demand lower latency data access than cloud storage can provide. For this use case, it makes sense to leverage instance types with local disk and to have a local HDFS available. This cloud deployment pattern looks very similar to on-premises deployments; however, it comes with an added benefit: access to cloud storage. As a result, a cluster can have tables that store the most recent data in partitions locally in HDFS, while older data resides in partitions backed by cloud storage, providing a form of storage tiering.  For example, the most recent month of sales data could reside in partitions backed by local HDFS storage providing the fastest data access, and data older than one month could reside in cloud object storage.

In summary

Deploying data marts in the cloud can help an organization be more agile with BI and data analytics, allowing individuals and teams to provision their own compute resources as needed while leveraging a single, shared data platform. If you're interested in learning more about how to architect analytic workloads, including the core elements of data governance, for the cloud, be sure and attend my talk at Strata Data in Singapore, Rethinking data marts in the cloud: Common architectural patterns for analytics.

Continue reading Rethinking data marts in the cloud.

http://ift.tt/2zxjfI8