Kudos to LakeFS for bringing order to the data tools ecosystem. They systematically organized common tools into a coherent framework, guided by tools’ roles within modern data pipelines. This intelligent approach makes the landscape more accessible for exploration.
Technological advancements and the exponential generation of data contribute to the intricacy of the realm of data. As businesses embrace the modern data stack, they encounter a spectrum of tools, each playing a pivotal role in converting raw data into actionable insights.
Now, let’s delve deeper into the data ecosystem and explore the most valuable tools within the modern data stack.
Data Pipeline Architecture
Think of data pipeline architecture as a structured roadmap for data flow. It outlines how data is collected from different sources, refined, and directed to where it’s needed. From gathering raw data to processing, enhancing, and using it, well-designed data pipelines are crucial for business excellence.
Three key factors indicative of a well-structured pipeline:
- Rate or Throughput: This indicates how much data a pipeline can handle within a specific time frame. It’s essentially the measure of how fast data can be processed.
- Data Pipeline Reliability: This involves ensuring that individual components within a data pipeline can handle errors gracefully. A dependable data pipeline includes auditing, logging, and validation mechanisms to maintain data quality.
- Latency: Latency refers to the time a single piece of data takes to traverse the pipeline. It’s more about response time than the amount of data processed. Achieving low latency can be costly in terms of both expenses and processing resources, so finding a balance is important to optimize analytics value.
Data engineers should focus on optimizing these aspects of the pipeline to align with the organization’s requirements.
Below are some of the widely used solutions in modern data pipelines and the roles they fulfill.
Data Storage and Processing
Data lakes and warehouses take center stage in modern data infrastructure. They serve as the reservoirs of your organization’s historical behavioral and transactional data. These data storage systems are designed with a focus on key aspects: effortless serverless auto-scaling, blazingly fast performance, economic efficiency at scale, enhanced data governance, and a boost to developer productivity.
Here are some helpful solutions for data storage and processing:
- Snowflake. This was the pioneer in cloud data warehousing. What’s neat about Snowflake is that it offers a flexible payment system for data teams. You pay separate fees for storing and computing data.
- Google BigQuery. This is Google’s cloud data warehouse. It’s designed to be super quick because it processes things all at once. Plus, it has separate storage and processing, so it can handle big tasks without slowing down.
- Amazon Redshift. A popular choice, Redshift works hand-in-hand with Amazon Web Services (AWS). It’s known for playing well with other data tools and is widely used.
- Firebolt. This cloud data warehouse works with SQL and claims to be way faster (up to 182 times faster) than others. They use some smart tricks to make data handling smoother and faster.
- Azure Synapse. If you’re into all things Microsoft, this is for you. Azure is a big player in the cloud computing game, especially if your team is all about Windows.
- Databricks. If you’re all about Apache Spark, you’ll love Databricks. It’s like Spark’s best friend and gives you the best of both worlds – structured and unstructured data – all in one place.
Data Ingestion
Data ingestion tools help to extract data from diverse sources effectively. The most common way to get data from a source is using API. However, before you this, you need to profile data: understand the nature of it you have and figure out what exactly do you need to extract.
Batch Data Ingestion:
- Fivetran. A top-tier enterprise ETL solution that makes data movement a breeze, seamlessly transporting data from source to destination.
- Singer. An open-source gem for shifting data across platforms, designed to ensure your data finds its new home effortlessly.
- Airbyte. Simplifying data sync from applications, this open-source platform is a go-to for hassle-free data movement.
- Datuum. Allows to automate data mapping and transform source schemes according to the target, including legacy systems.
Streaming Data Ingestion:
- Apache Kafka (Supported by Confluent). The open-source event streaming platform, expertly managed by Confluent, is designed to ace streaming analytics and data intake. Their recent addition of Apache Flink speaks volumes.
- Amazon Kinesis. Amazon’s streaming solution, tailor-made for those hooked into Redshift as their data hub.
- Google Pub/Sub. A GCP wonder that effortlessly ushers streaming data into BigQuery, data lakes, or operational databases. A noteworthy mention is Google’s direct Pub/Sub-to-BigQuery stream, waving goodbye to pipelines.
- Apache Spark. The all-in-one analytics engine for heavy-duty data processing. Open-source and trusted for streaming scenarios, often coupled with Databricks for that extra edge.
Data Orchestration
Data orchestration tools are used for end-to-end data workflow management, including scheduling data tasks, managing dependencies within various layers, and monitoring all processes.
- Airflow. Widely embraced by data teams, Airflow serves as an intelligent scheduler for your data workflows. It empowers you to efficiently schedule, monitor, and oversee data movement across tasks or stages, ensuring precise sequencing and timing.
- Other prevalent orchestration options encompass Prefect, Dagster, and Mage.
Data Transformation
Data transformation serves to standardize, replicate, delete, or restructure data. In the modern data stack, transformation tools streamline these processes by minimizing repetitive tasks, offering consistent data model design frameworks, and enhancing code reusability and testability.
Monsters that have long been on the market:
- Oracle. With a global reach, these products have garnered a customer base of over 16,000 organizations. Oracle stands out for its harmonious approach, balancing focus between OCI and multi-cloud deployments.
- IBM. IBM’s remote runtime capabilities have earned accolades from clients. These capabilities trim egress costs by enabling developers to construct pipelines once and then channel workloads to the execution environments they prefer.
- Informatica. With comprehensive pre-built libraries, efficient workflow orchestration, and broad connectivity, Informatica excels in meeting diverse transformation needs.
Middle tier:
- Talend. Data engineers opt for Talend due to its versatility across data sources, enriched by pre-built transformation libraries and streamlined workflow orchestration.
- Microsoft is the most suitable data integration tool choice when selecting an Azure-native data ecosystem.
Newcomers:
- Matillion collaborates closely with leading data management vendors and system integrators like Snowflake, Collibra, Databricks, and ThoughtSpot.
- AWS Glue is favored among AWS ecosystem users seeking a reliable, user-friendly data transformation solution.
- Denodo focuses on distributed data architectures through source virtualization and data science notebooks. Its caching tier can be stored persistently, catering to logical data warehouse and data fabric needs with semantic models.
Business Intelligence and Analytics
BI and analytics tools democratize data by making it easy for everyone to quickly analyze, build reports, and use them for decision-making.
- Power BI. Microsoft’s solution seamlessly integrates with Excel and offers self-service analytics for your entire team.
- Tableau. Renowned for its user-friendly interface and leadership in BI.
- Looker. A big data-optimized BI platform for collaborative report and dashboard creation.
- Qlik supports visual data discovery, self-service BI reporting, and the development and sharing of data dashboards.
- Preset.io is open-source, user-friendly, and easily scalable across any data architecture.
- Amazon QuickSight optimizes productivity through its interactive GUI, shared visualizations, and seamless collaboration capabilities.
Data Governance
Data governance tools aim to foster transparency, collaboration, and data democratization. Data governance rests on two main pillars: observability and cataloging. Observability tools focus on tracking data health and real-time visibility into data pipelines. On the flip side, data cataloging tools serve the purpose of comprehending what data exists and where it’s located—a task that poses challenges for numerous organizations.
Data Observability:
- Monte Carlo. The leader and actually the creator of this category. It proactively identifies and prevents data pipeline issues, minimizing downtime and errors.
- Other tools worth mentioning: SODA and New Relic.
Data Cataloging:
- Atlan names itself Netflix for data, providing users with customized experiences according to their needs. Through Personas, each user gets an individual personalized homepage, custom metadata, and access to data curated to their workflows.
- Castor provides users with a powerful search of data assets and the possibility to reuse queries by teammates.
- data.world places a knowledge graph at the heart of its architecture, providing users with an intuitive UI and visual entry to data discovery, governance, and analysis.
- Talend Data Catalog automatically discovers and classifies data, making it easy for users to search and access what they need.
Access management
Ensuring data availability to those who truly need it is key in the modern data landscape. Access management solutions are crucial in safeguarding sensitive information, PII and complying with regulations like GDPR or CCPA. Leading vendors in this domain include:
- Immuta. Simplifies data policy management, accelerating secure data access.
- BigID. A versatile privacy, protection, and comprehensive data insights platform.
- Privacera. Streamlines data access, security, and policy management across multiple cloud services from one unified interface.
- Okera. Offers flexible enforcement methods and cross-environment data access control.
- SatoriCyber. Unleashes data teams’ agility by decoupling security, privacy, and access controls from the data layer.