What is the right database technology for your data warehousing needs?

>Used Azure Synapse Analytics for the data warehouse database - took advantage of MPP architecture for scalability. >Stored raw data in Azure Data Lake Storage for low cost - made it accessible to Synapse via linked service. >Set up Azure Data Factory pipelines to orchestrate and transform data from on-prem SQL Server and SaaS apps into the data lake. >Built aggregation tables in Synapse from the raw data for BI reporting. Used Polybase to efficiently load from Data Lake. >SYNAPSE analytics pools allowed for ad-hoc analysis and dashboarding, providing good concurrency. >Added Azure Redis Cache to improve performance of dimensional queries against star schema. >Automated warehouse tasks with Azure Logic Apps. Synapse, ADL, ADF for ETL.

Selecting the right database technology for data warehousing begins with assessing your data warehouse architecture. Various models, like centralized, decentralized, hub-and-spoke, data lake, or hybrid, offer distinct advantages and drawbacks. Consider factors like data source diversity, ingestion frequency, integration quality, and end-user analytical requirements. Opt for a database technology aligning with the architecture that best addresses your data warehousing goals and challenges. This strategic alignment ensures optimal performance, scalability, and efficiency in managing and analyzing data within your data warehousing environment.

Experiência Pessoal: Na constru??o de um data warehouse, considere a arquitetura que melhor atende às suas necessidades. Se optar por uma abordagem tradicional, como o modelo de "top-down", ou por uma arquitetura moderna orientada por nuvem, como o data warehouse as a service (DWaaS), dependerá das características específicas do seu negócio e da escalabilidade desejada. No caso de Oracle, optamos por uma arquitetura tradicional de data warehouse usando o Oracle Database. Utilizamos o Oracle Exadata para garantir desempenho otimizado e capacidade de expans?o. A implementa??o de um Oracle Real Application Clusters (RAC) permitiu distribuir a carga de trabalho de maneira eficiente em vários nós para atender a picos de demanda.

Como área tecnologia tudo é evolu??o. Arquitetura Data Warehouse evoluir muito nos últimos anos. Anos 80 criou o conceito. Era utilizado banco dados relacionais como tecnologia principal. ETL era forma de realizar cargas dados entre sistemas OLTP para OLAP. Processamento Batch era principal forma de realizar inclus?o dados entre ambientes. Hoje em dia é usado um híbrido entre arquiteturas, Data Lake e Data Lake House mais utilizadas hoje em dia. Processamento em bacth e real time e Neal real time... dominam as principais formas de realizar processamento nesse novo modelo. Conceito ELT também entrou com grande for?a no mundo BIG DATA.

When building a data warehouse, one needs a database that can handle large volumes of data and complex queries. In my experience, the following are the best; 1. Relational databases like SQL Server, Oracle, and Teradata. These are designed for transactional and analytical workloads in large enterprises. They allow for complex SQL queries against structured normalized data. 2. Columnar databases like Vertica and Amazon Redshift. These store data by columns instead of rows. 3. Data warehouse appliances like Teradata, Oracle Exadata, and IBM Netezza. These are optimized hardware/software bundles designed just for data warehousing scenarios.

In the quest for the right database technology for data warehousing, the schema plays a pivotal role. It defines the logical structure, encompassing tables, columns, relationships, and constraints. Schemas like star, snowflake, galaxy, or vault offer distinct advantages based on data complexity, granularity, dimension diversity, and normalization levels. The chosen database technology should seamlessly support the preferred schema, aligning with your data warehousing design and analytical requirements. This strategic alignment ensures efficient query performance, flexibility, and overall effectiveness in managing and analyzing data within your data warehousing environment.

>Designed a star schema with dimensional tables for customer, product, time, and location. Allows for fast queries focused on dimensions. >Fact tables store quantitative metrics like sales, units sold. Key dimensions connected to facts. >Slowly changing dimensions like customer handle historical changes via Type 2 technique. >Polybase and partitioned tables used to optimize large fact table loading and archival. >Applied columnstore compression on large tables for 10x data compression. >Indexed tables on query filter columns to optimize search speed. >Denormalized redundant data in dimensions to limit table joins. star schema design optimized for analytics workloads, Polybase, columnstore, and partitioning to boost query performance.

Escolhemos um modelo dimensional usando o Oracle Data Warehouse. Criamos tabelas de dimens?o e fato, aplicando índices bitmap para facilitar consultas analíticas. O particionamento de tabelas foi implementado para gerenciar grandes volumes de dados históricos com eficiência.

>Primary workload is analytics - building reports, dashboards, and models off the data warehouse. Ad-hoc querying directly on Synapse via BI tools. >Daily batch ETL workload - transferring data from transaction systems into the warehouse. Data Factory handles orchestration and transformation. >Minimal transactions directly on the warehouse - most inserts and updates happen upstream before loading. >High concurrency expected during business hours - provisioned pools appropriately to handle concurrent users. >Workload is read-heavy vs write-heavy. Applied columnstore optimization for analytics read speed. >Query patterns target recent data more than historical - partitioned tables by date for quicker searches.

When selecting a database technology for data warehousing, assessing the workload is paramount. Workload, defined by the data processing and analysis volume and type, significantly impacts resource consumption and database performance. Workloads vary—batch, real-time, interactive, or analytical—each with distinct demands based on data volume, query complexity, user concurrency, and database scalability. Choose a database technology aligning with the workload that best suits your data warehousing operations. This strategic alignment ensures optimal performance, scalability, and reliability, meeting the expectations of your data processing and analytical requirements.

Ao enfrentar cargas de trabalho flutuantes, implementamos Oracle Automatic Workload Repository (AWR) para monitorar o desempenho em tempo real. Utilizamos o Oracle Enterprise Manager para ajustar automaticamente recursos, garantindo que o sistema pudesse lidar com varia??es na demanda sem interven??o manual.

Carga Trabalho depende da necessidade. Principais pontos a considerar. Posso processar tudo em um determinado horário do dia? Fa?a uso Processamento Batch. Job schedulado em alguma ferramenta execu??o em determinado horário. Informa??o precisa ser disponibilizado para equipe analítica em tempo real? Banco dados necessita usar conceito chamado CDC(change data capture). Incluindo alguma ferramenta Kafka ou RabbitMQ(Fila).

Choosing a database technology for data warehousing involves a critical assessment of the technology itself. Options include relational, non-relational, or hybrid databases, each with distinct features and limitations related to storage model, query language, indexing method, compression technique, partitioning strategy, and optimization algorithm. Select a database technology aligned with your data warehousing requirements and preferences, ensuring it provides the necessary features and capabilities. This strategic decision contributes to the overall effectiveness and efficiency of your data warehousing operations.

>Azure Synapse Analytics provides the SQL database engine - used for dimensional modeling, ETL, and analytics. Provides necessary MPP performance. >Azure Data Lake Storage houses the raw data - made it accessible to Synapse via linked service. Low cost store. >Polybase utilized to load from Data Lake into Synapse tables. Handles scale, speed. >Azure Data Factory for data orchestration and transformation - serverless ETL tool. >Azure Databricks for custom data processing and machine learning - integrated with pipeline. >Power BI and other BI tools connect to Synapse for visualization - provides dashboards. >Azure Redis Cache to cache frequent dimension queries - improves performance.

Ao explorar tecnologias, escolhemos o Oracle Autonomous Data Warehouse (ADW) para uma abordagem na nuvem. A capacidade de dimensionar automaticamente recursos e o gerenciamento simplificado através do Oracle Cloud Console proporcionaram uma solu??o eficiente e fácil de administrar.

Algumas tecnologias utilizadas para Data Warehouse. Bancos Relacionais Conceito mais Tradicional(Oracle,Postgres,Mysql, SQL SERVER). Data Lake armazenamento Disco próprios com tecnologias para acessar os dados mais rapidamente.(S3 AWS,Azure Data Lake Store Gen2 Azure, Cloud Storage Google) Ferramentas Data Warehouse Nuvem -> Big Query - Google. Azure Synapse - Azure. RedShift - AWS.

When selecting a database technology for data warehousing, the cost is a crucial consideration. This encompasses expenses related to acquisition, maintenance, and utilization, covering aspects like hardware, software, licensing, storage, network, compute, support, and maintenance. Costs vary based on database type, size, configuration, vendor or provider, service level agreement, pricing model, subscription plan, and usage demands. Opt for a database technology aligning with your budget and needs, ensuring optimal value. This strategic approach guarantees that your data warehousing investment is both cost-effective and tailored to your organizational requirements.

>Synapse Analytics charges per DWUs provisioned and data stored - right sized capacity for needs. >Data Lake Storage is low cost for raw data - $0.023/GB/month in hot tier. Archived older data. >Data Factory has competitive pay-as-you-go pricing - monitored pipeline usage. >Reduced overall data volumes with columnstore compression - 10x compression savings. >Reserved Instances used for predictable workloads - offered up to 65% discounts. >Optimized ETL/ELT logic to reduce data movement - avoided unnecessary data transfers. >Scheduled workloads for off-peak times to lower compute charges. >Analyzed usage trends to right size resources - scaled up/down as needed. Continuous optimization of consumption and use of discounts can reduce TCO.

Ao avaliar os custos, consideramos a oferta de Oracle Cloud Infrastructure e seu modelo de pagamento por uso. Embora tenhamos inicialmente implementado no local com Oracle Exadata, a análise de custo-benefício nos levou a migrar para o Oracle Cloud ADW para reduzir os custos operacionais.

Durante a avalia??o, realizamos testes comparativos de desempenho usando o Oracle SQL Performance Analyzer. Analisamos consultas complexas, ajustamos índices e otimizamos consultas para garantir eficiência. A integra??o transparente com ferramentas de análise, como Oracle Analytics Cloud, facilitou a tomada de decis?es baseada em dados.

Consideramos a seguran?a com Oracle Transparent Data Encryption para proteger dados confidenciais. Participamos ativamente da comunidade Oracle, como o Oracle Developer Community, para trocar conhecimentos e resolver desafios específicos da implementa??o. A integra??o com Oracle Machine Learning permitiu explorar análises avan?adas diretamente no data warehouse.

Escolha qual ferramenta ou Storage é complexo devido uso BI, Data Science e Desenvolvimento Orientado Cloud Native. Minha opini?o antes de buscar uma ferramenta ou tecnologia. Busque entender conceitos Storage, Data Lake, Lake House e indexa??o dados. Outro ponto importante para criar um Data Warehouse é entrevistar os usu?rios usaram solu??o.