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Organizations have been using data warehouse and business intelligence (DWBI) for many years to support business decision making. These workloads are delivered to the Amazon Web Services () environment to take advantage of the cloud. However, these workloads are built using multiple vendor tools and technologies, and the customer bears the administrative burden.
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This post provides architectural guidance for integrating multiple DWBI technologies into Managed Services to help reduce administration, deliver operational simplicity, and business efficiency. Two conditions are tested:
Organizations are involved in the management of many DWBI technologies due to acquisition, integration, and promotion and change of operation. This workload uses extract, transform, and load (ETL) tools to read relational data from upstream databases, process it, and store it in a data warehouse. After that, many of these operations use business intelligence tools to generate valuable insights and present them to users in the form of reports and dashboards.
This DWBI technology is usually installed and stored on its own server. Figure 1 shows the increase in organizational management but also creates challenges in maintaining the overall knowledge of the group.
Each of these tasks can be successfully performed using a service. For example, Glue can be used for ETL, Amazon Redshift for data warehousing, and Amazon QuickSight for business intelligence.
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Through the use of the mentioned services, organizations will be able to consolidate their use of DWBI technology. Organizations will also be able to quickly adapt to these services, as their engineering team can easily apply their DWBI knowledge to these services. For example, applying SQL knowledge to Glue jobs with SprakSQL, to Amazon Redshift queries, and to Amazon QuickSight dashboards.
Figure 2 shows the redesigned architecture of Figure 1 using services. In this architecture, ETL functions are integrated into Glue. Glue crawler is used to automatically organize the source and metadata of the target table; then, Glue ETL functions use these catalogs to read data from the source and write to target (data warehouse). Glue functions also apply necessary transformations (such as joins, sorting, and grouping) to the data before writing. Additionally, the Glue trigger is used to schedule the execution of a task. Alternatively, Apache Airflow Managed Workflow can be used to schedule tasks.
Similarly, data storage functionality is integrated with Amazon Redshift. Amazon Redshift is used to store and organize advanced data and also implement appropriate data access control for both workloads and users.
Finally, business intelligence activities are integrated using Amazon QuickSight. It used to create the necessary dashboards that received data from AmazonRedshift and used sophisticated business logic to generate the charts and graphs needed to get business insights. It is also used to apply necessary access restrictions to dashboards and data.
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In the case where the data sources are in the local datacenter, the overall solution will be the same as Scenario 1, with the additional step of moving the data continuously from the database to the Amazon Simple Storage Service (Amazon S3) bucket. Data migration can be efficiently handled by a Data Migration Service (DMS).
To make the source database accessible to the DMS, a connection needs to be established between the cloud and the local network. Based on performance and operational needs, an organization can choose a Direct Connect service or a Site-to-Site VPN service to move data securely. For the purpose of this discussion, we are considering Direct Connect.
In Figure 3, the DMS function is used to perform a full load followed by a switch to capture data to continue moving data to the S3 bucket. In this case, Glue is used to catalog and read data from an S3 bucket. The rest of the data flow is the same as described in Scenario 1.
Also, the updated architecture provides the necessary security through access control, data encryption at rest and in motion, monitoring, and auditing.
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Additionally, both Amazon Redshift and Amazon QuickSight provide their own authorization and access controls. Therefore, a user can be a local or collective user. With the help of this authentication, the organization will be able to control access to data in Amazon Redshift and access the dashboard in Amazon QuickSight.
In this blog post, we discussed how Glue, Amazon Redshift, and Amazon QuickSight can be used to integrate DWBI technologies. We also discussed how the architecture can help an organization build a scalable, secure workload with automated scaling, access control, log monitoring and performance monitoring. You need to understand your data, and you need a budget-friendly way to do so. , then an open source Business Intelligence tool is obviously the one for you.
But before trying a bunch of open source BI tools (which can be time-consuming, even with an article like this explaining the pros and cons), let’s take a step back and dive deeper into segmenting the BI space.
We insist because having a local taxonomy of business intelligence is useful, allowing you to quickly position the tool in the first few minutes of browsing a merchant’s website. The business intelligence landscape can be incredibly confusing because tools from previous paradigms last a long, long time, so having a classification system in your head cuts through all that noise.
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Explaining the difference between SQL and non-SQL BI, or Modeling vs non-visual BI would make a wall of text, so we should make a blog post dedicated to that. Explore: Navigating the business intelligence space – the complete guide.
Preset is a fully hosted BI tool for Apache Superset. Apache Superset is an open source data visualization and data visualization software application capable of handling data at the petabyte scale. The setup was previously started as a hackathon project at Airbnb in the summer of 2015.
Lightdash is a new open source business intelligence solution that can connect to a user’s dbt project and allow them to add metrics directly to the data transformation layer, then create and share insights with the rest of the team.
Comes with a code-based modeling layer with self-service data inspection. it’s 100% cloud-based, provides a centralized data modeling approach for BI teams, and empowers business users who don’t know SQL can do
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Similar to Lightdash and Looker, it’s also dev-friendly: You can write code (DSL) to define your statistical thinking, and check them in Git version control for better governance, or synchronize your logic with dbt integration. Also, it has a pay-as-you-go pricing model so it’s risk-free.
Helical details an open source BI tool with a very unique approach to self-service analytics by introducing a BI platform that allows end users to use an API to add any functionality as per their needs.
On Capterra, Helica Insights has a rating of 5.0. Many users like their clean design and report visuals. Customer support is also cited as one of Helica’s forte.
BIRT, which stands for “business intelligence reporting tool”, is an open source and flagship software project within the Eclipse Foundation. BIRT pulls data from a variety of data sources that can be used for reporting and visualization.
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Reviews on Capterra reveal that while BIRT is functionally adequate for reporting, its analytical capabilities and customer service often fall short of expectations and there is a steep learning curve.
Jaspersoft is a customizable and developer-friendly Business Intelligence platform that allows developers to create analytics solutions that meet business needs.
On Capterra, Jaspersift has a rating of 4.3. Many users praised the tool for being highly customizable and generally ideal for Java developers. On the other hand, other lack of community support for some issues and clumsy interface design are Jaspersift’s weaknesses.
KNIME is scalable enterprise-grade software focused on empowering data science teams to create real business value. Knime offers powerful tools that allow data teams to collect, model data and to feed and manage results all the way to visualization and data generation.
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On social media, KNIME has a rating of 4.6/5. Review the best of a variety of native data processing tools, user-friendly UI and machine learning capabilities. Limited visualization options and high memory usage were cited as major turn-offs for users.
Related Topics: Best Advanced Reporting Tools for Microsoft SQL Advanced Analytics and BI Tools for Snowflake BI and Reporting Tools for Amazon Redshift: Best Open Source Analytics & Affordable Alternatives 07. SpagoBI
SpagoBI is an open source BI tool that allows users to aggregate and disaggregate traditional data and big data sources into actionable insights through data exploration, data preparation, self-service data, ad-hoc reporting, and much more.
On review platforms like Capterra or G2, users generally
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