April 2023 Summaries

Fivetran is a tool used by HubSpot's analytics engineering team, led by Oviya Arasu, to automate pipelines and decrease engineering bottlenecks in the company's people data analysis process. Previously, this required manual work with other teams, but with Fivetran, Arasu can access clean, modeled data efficiently and autonomously, controlling all necessary data sources. The tool integrates well with dbt Core, providing pre-built data models, Transformations, and orchestration capabilities that expedite foundational parts of data transformations, freeing up time for more advanced exploration. By leveraging Fivetran's pre-built packages, Arasu can customize models and automate the process, saving significant time on basic tasks and allowing her to focus on custom logic and unique data sources.

Fivetran's Automated Data Movement Platform has partnered with Azure Synapse to provide a seamless integration for accelerating data movement, offering a "set it and forget it" experience. This partnership enables Fivetran to support over 450+ sources, including databases, applications, files, and events, making it easy for Microsoft customers to move their data into Azure Synapse. The platform's fast, simple, and self-serve approach allows users to create new data pipelines in under 10 minutes, with minimal effort required from data engineering and development teams. Fivetran also supports other Microsoft Azure destinations and sources, including Azure Databricks, Azure SQL Database, and Azure Blob Storage. The company's Local Data Processing (LDP) solution provides continuous replication capabilities, allowing for high-volume data movement without latency constraints. By using Fivetran with Azure Synapse, businesses can accelerate their ability to deliver scalable and sustainable data products and services, while also improving operational sustainability and reducing costs.

Building a world-class data operations team requires careful planning and consideration of several key factors. First, it's essential to rule out barriers to getting started, such as small scale or complexity, performance or regulatory compliance standards, and specialized software production. Next, organizations must decide whether to migrate or start fresh, considering the cost of ending existing contracts and compatibility with products and services intended to be kept. Evaluating tools and technologies is crucial, including data warehouses, integration tools, business intelligence platforms, and transformation layers. Calculating total cost of ownership and ROI involves comparing existing workflows with alternatives and considering costs, benefits, and opportunity costs. Establishing success criteria based on time, money, labor savings, expanded capabilities, successful execution of new projects, reduced turnaround time for reports, reduced data infrastructure downtime, greater business intelligence usage, and new available and actionable metrics is also vital. Finally, setting up a proof of concept involves testing products in a low-stakes manner to measure performance against standards for success.

Fivetran is a tool designed to help small data teams centralize and manage their data efficiently. These teams, often consisting of only one or three people, are responsible for turning disparate data into recurring reports, ad-hoc analysis, financial ledgers, activations, and more. Fivetran's solution involves using ready-built connectors to save time and resources, starting with a free plan that offers low-cost usage-based pricing. This allows teams to scale their data practice without worrying about unknown costs. Additionally, Fivetran provides quickstart data models and dbt integration to automate data transformations, freeing up time for more custom or higher-impact projects. The tool also extends its support team to help with data-driven problems, connecting users with peers through the community. By implementing Fivetran's tools, small data teams have achieved significant efficiency gains, reduced time to get insights, and improved their ability to track performance and operational costs.

Fivetran and Hevo Data are two powerful no-code tools that empower data teams to build quickly, but they differ in detailed work of data integration, costs, and capabilities. Fivetran offers fully managed data connectors to sync data from over 300 SaaS applications, APIs, databases, and other structured and semi-structured data sources into target analytical destinations like data warehouses and data lakes. Hevo Data is a bidirectional cloud-based data pipeline platform with modern features like no-code configuration, easy pipeline setup and maintenance, log-based replication, and pre- and post-transformation capabilities. Fivetran has over 300 connectors compared to Hevo Data's 150, but the number of connectors is not the only consideration; intersectionality of data sources, custom connectors, and scalability are also key factors. Fivetran offers more advanced features, including capturing deletes, custom data replication, data blocking, and column hashing, while Hevo Data has powerful sync and transformation capabilities with log-based replication, reverse ETL, and bi-directional syncs. Pricing models for both platforms are consumption-based, but Hevo's is use-it-or-lose-it every month, whereas Fivetran's MARs roll over into future months within the same year and users are not charged for transformations. Ultimately, companies requiring scalability, extensibility, and more capable connectors may find Fivetran more advantageous, while smaller organizations or those with lower data maturity needs may prefer Hevo Data.

Fivetran is partnering with Databricks on their launch of Lakehouse for Manufacturing (L4M), a solution designed to help manufacturing organizations unlock the full value of their data. The platform enables the unification of diverse data sources, providing a flexible and scalable architecture that can handle complex IoT, images, and video data. Fivetran's automated data movement solution makes it easy for manufacturers to consolidate disparate on-prem, cloud, and IoT enterprise data sources in real-time to Databricks L4M. This partnership aims to empower manufacturers to make quick, informed decisions without sacrificing depth of analysis or time, while also bringing together supply chain data to find areas of efficiency and boost industrial productivity. With Fivetran and L4M, manufacturing organizations can gain a singular view of their customers, scalable forecasts, and increased sales by analyzing customer behavior and insights. The solution provides an integrated AI capabilities and pre-built solutions, making it easy for teams to work together without creating a data bottleneck.

The importance of developing a data strategy cannot be overstated, as it can optimize businesses, meet regulatory demands, deal with economic challenges, and benefit from AI technologies. Without a strategic approach to data, organizations face increased risks such as project failure, inability to respond to market changes, and security threats. Experts emphasize the need for business alignment, access maturity, governance, integrated solutions, scalability, and diversity in the data team. A successful data strategy has six key elements that can help organizations embrace AI technologies, comply with regulations, and achieve their business objectives. By prioritizing a well-defined data strategy, businesses can unlock the value of their data and stay ahead in an increasingly data-driven world.

Developing an effective data strategy is crucial for organisations to optimise their businesses, meet regulatory demands, deal with economic challenges, and benefit from AI technology. Experts agree that a strategic approach towards data is necessary to address major challenges such as climate change, security threats, and market disruption. A successful data strategy has six key elements: business alignment, access maturity, a data framework, governance, integrated solutions, and scalability. It requires collaboration between departments, well-defined success criteria, and a skilled and diverse data team. Moreover, organisations must consider the entire lifecycle of AI deployments and prepare their data strategy with regulatory compliance in mind to unlock the benefits of AI and achieve their business objectives.

The modern data lake is the product of three market trends: durability, availability, scalability, cost optimization, ease of data ingestion, and cold storage. It converges with data warehouses to manage or modify data as in a relational system, and emerges with central cataloging and governance capabilities. Traditionally, data lakes were at risk of becoming dumping grounds without metadata, making it hard to address concerns like ensuring extracted data reaches its destination, accounting for dirty writes, identifying and removing customer records, and maintaining privacy. Fivetran + AWS Lake Formation + Apache Iceberg governs data with ACID compliance, enabling data governance through open table format, metadata management, column- and row-level access, and object-level security controls. This ensures security, enables the "right to be forgotten," and makes managing individual records for regulatory compliance easier.

Replacing iPaaS workflows with warehouse-centric data pipelines is a strategic move for organizations looking to streamline their data integration and reduce manual workloads. The modern data stack, including cloud data warehouses like Snowflake, BigQuery, and Databricks, has solved key challenges such as consolidating customer data into a single source of truth. Fivetran and Hightouch have introduced fully managed ELT connectors and declarative models that enable organizations to automate data extraction, transformation, and loading into their warehouses. This approach simplifies workflows, reduces dependencies, and ensures all operational systems are in sync with one another, powered by a single source of truth. By adopting warehouse-centric pipelines, companies can eliminate the need for point-to-point integrations, streamline their architecture, and focus on delivering value to their organization and customers.

SQL is a powerful and versatile programming language that is widely used in the field of data analysis due to its ability to support easy data manipulation, be an easy-to-learn language with a large active community of users who can provide support and resources. SQL allows data analysts to work with modern data tools such as Talend, Alteryx, Tableau, Looker, and Google Data Studio, making it easier to use these tools in their work. It also enables the management and manipulation of large datasets, provides the ability to derive deeper insights by joining data sets, automates data processing tasks through stored procedures and functions, and increases earning potential as SQL is a common skill for high-ranking professionals across industries and roles. Additionally, SQL skills can be leveraged with frameworks such as dbt to perform data transformation and manage pipelines at scale.

Fivetran has partnered with Snowflake to accelerate manufacturing insights by enabling fast and secure access to data from SAP systems, allowing manufacturers to break down silos and drive better decisions with real-time analytics. The partnership uses Fivetran's data transformation capabilities and Coalesce's scale-built tool to help companies like Powell Industries automate data ingestion and transformation, resulting in faster and more meaningful insights. With the Snowflake Manufacturing Data Cloud, manufacturing organizations can easily access and analyze data from various sources, including ERP systems and shop floor MES systems, to gain a single source of truth and improve supply chain visibility. By leveraging this modern data stack, companies can make agile, data-driven decisions and overcome common challenges like lack of collaboration across suppliers/customers and limited visibility into their supply chains. Fivetran's partnership with Snowflake has helped customers like Coke One North America achieve significant improvements in data processing speed and accuracy, enabling them to turn meaningful insights into action.

Fivetran, an automated data movement platform, addresses the challenge of siloed and unstructured data by providing over 300 pre-built, no-code connectors that centralize data into cloud data warehouses while managing schema drift, updates, security, and governance. Despite this centralization, data teams often struggle with making data usable for decision-making, highlighting the importance of data modeling, which involves transforming raw data into analytics-ready tables through cleansing and structuring. This process enhances data quality, standardization, and efficiency, allowing integration with other systems and enabling timely data activation. Fivetran's new Quickstart data models simplify this transformation into activation-ready data without requiring dbt projects or third-party tools, streamlining workflows and reducing manual effort. In partnership with Census, Fivetran helps companies like Canva, Figma, and Zip enhance marketing personalization and sales outreach, demonstrating the potential for scalable and efficient data practices.

There is a growing need for data leadership as businesses navigate economic uncertainty and focus on efficiency in the coming year. Data plays a crucial role in reviewing products, services, and customer relationships to ensure efficiency does not impair them. A well-defined data strategy can help organisations leverage a data-driven approach, identify industry trends, and uncover friction in business models and processes. To succeed, senior leadership teams must embrace data and data leaders should take their place alongside traditional leaders at the top table. Data leaders need to align their work with business priorities and ensure all areas of the organisation know how to work with the data team to garner value from insights generated. Leading businesses are using data to gain a deeper understanding of every element of the organisation, informing investment decisions and creating truly differentiated customer and employee experiences.

Fivetran is a cloud-based automated data movement platform designed to extract, load, and transform data between various sources and destinations. It offers an easy-to-use interface for end-users to effortlessly move data, eliminating the need to build complex solutions from scratch. The platform's architecture is divided into user-local environment, Fivetran cloud, and customer cloud, ensuring security and performance. Fivetran achieves incremental updates through change data capture (CDC) and idempotence, and addresses schema drift handling through live updating. It also prioritizes pipeline and network performance by minimizing latency and bottlenecks. Beyond being a point solution for centralizing data, Fivetran provides security, governance, and extensibility features to enable organizations to democratize access to data and monetize it, ensuring regulatory compliance, managing brand risk, protecting intellectual property, and safeguarding customer information.

Fivetran Inc. emphasizes the importance of security in data platforms, highlighting five key criteria to evaluate when deciding which platform to adopt: compliance with laws and regulations, column masking to obscure sensitive data, access control for external parties and internal users, end-to-end encryption of data in transit and credentials, and flexible deployment methods that accommodate various cloud providers and regions. These features are crucial for maintaining regulatory compliance, managing brand risk, protecting customers' identities and sensitive data, ensuring system availability, and upholding basic ethics by preventing the exposure of sensitive data.

Fivetran's "dbt smart run" automates the process of running only necessary dbt models to save time and cost, eliminating the need for manual scripting or complex commands. By copying relevant tables from production to development schema and ignoring unnecessary models, analysts can focus on understanding the impact of changes without wasting brainpower. This tool provides a simple python command that handles all the heavy lifting, allowing users to specify target models and letting "dbt smart run" figure out the rest.

Fivetran has partnered with Monte Carlo to provide data observability across its modern data stack, allowing users to accelerate data incident detection and resolution through pipeline monitoring from source to destination. This integration gives visibility into upstream problems before they impact downstream users and products, enabling faster troubleshooting of issues related to accuracy and efficacy of data pipelines. With the new partnership, users can quickly understand the health of individual Fivetran connectors and identify interruptions to regularly scheduled loads, gaining full visibility with automatic column-level data lineage that incorporates multiple sources such as warehouses, lakes, dbt, Airflow, BI tools, and now Fivetran. This integration is expected to reduce data downtime and help businesses tackle initiatives that move the needle for their business.

As companies expand and their client lists grow, targeting the right audience becomes crucial for capitalizing on growth opportunities and maximizing revenue through strategies like upselling, win-backs, trial conversions, and engaging highly active users. To effectively manage these efforts, leveraging first-party data stored in data warehouses is essential, but accessing and utilizing this data can be challenging due to technological barriers and siloed information. Tools like Fivetran Activations provide a solution by enabling marketers to activate and use data without needing coding skills, facilitating personalized and targeted marketing campaigns. By using such tools, marketers can efficiently engage with various audience segments, such as upsell candidates, churn risks, trial users, and advocates, ultimately enhancing customer retention and driving brand growth.

Fivetran has released a new offering that supports Amazon S3 as a destination with Apache Iceberg, accelerating data delivery to the cloud storage service with secure, fully managed pipelines and comprehensive compliance. The new solution is designed to simplify data ingestion into data lakes by automating tasks such as cleansing, normalizing, and loading data into the lake. This enables organizations to unlock the full value of their data without manual work or time-consuming efforts. With Fivetran's support for Amazon S3 with Iceberg, businesses can reduce their reliance on custom ETL code and ongoing maintenance, while also addressing challenges related to governance, security, and automation. The solution provides a compliant and secure data lake with support for atomic transactions and granular access control, anonymizing personally identifiable information and providing reliable data transformation. By automating pipeline management and governance, organizations can reduce the time to insight and unlock the full potential of their data lakes.

Fivetran's Managed Data Lake Service for Amazon S3 aims to simplify and secure the process of accessing data in a data lake, building on its mission to make data access as easy as electricity. The service automatically delivers clean, organized, and standardized data from over 600 sources into an Amazon S3 data lake, ensuring reliable, governed, and high-quality data at scale. By leveraging the scalability, availability, security, and performance of Amazon S3, Fivetran's solution focuses on governance, security, and automation to effectively manage and address challenges related to managing a data lake. The service provides a compliant and secure data lake with support for ACID transactions and granular access control through AWS Glue and Unity Catalog integrations. It also automates the extraction, cleansing, deduplication, and preparation of large datasets, allowing organizations to maintain reliable, governed, and high-quality data in their data lakes.