Data-driven organizations are poised for success. They can make more efficient and accurate decisions and their employees are not impeded by organizational silos or lack of information. Data replication enables leveraging data to its full extent. But how can organizations maximize the potential of data replication and make sure it helps them meet their goals? Read on for all the answers.

Data replication is the process of copying or replicating data from the main organizational server or cloud instance to other cloud or on-premises instances at different locations. Thanks to data replication, organizational users can access the data they need for their work quickly and easily, wherever they are in the world. In addition, data replication ensures organizations have backups of their data, which is essential in case of an outage or disaster. In other words, data replication creates data availability at low latency.

Data replication can take place either synchronously or asynchronously. Synchronously means the data is constantly copied to the main server and all replica servers at the same time. Asynchronous data replication means that data is first copied to the main server and only then copied to replica servers. Often, it occurs in scheduled intervals.

Data replication ensures that organizational data is always available to all stakeholders. By replicating data across instances, organizations can ensure:

Scalability

Data scalability is the ability to handle changing demands by continuously adapting resources. Replication of data across multiple servers builds scalability and ensures the availability of consistent data to all users at all times.

Disaster Protection

Electrical outages, cybersecurity attacks and natural disasters can cause systems and instances to crash and no longer be available. By replicating data across multiple instances, data is backed up and always accessible to any stakeholder. This ensures system robustness, organizational reliability and security.

Speed / Latency

Data that has to travel across the globe creates latency. This creates a poor user experience, which can be felt especially in real-time based applications like gaming or recommendation systems, or resource-heavy systems like design tools. By distributing the data globally it travels a shorter distance to the end user, which results in increased speed and performance.

Test System Performance

By distributing and synchronizing data across multiple test systems, data becomes more accessible. This availability improves their performance.

Organizations that have multiple branch offices across a number of continents can benefit from data replication. If organizational data only resides in servers in Europe, users from Asia, North America and South America will experience latency when attempting to read the data. But by replicating data across instances in San Francisco, São Paulo, New York, London, Berlin, Prague, Tel Aviv, Hyderabad, Singapore and Melbourne, for example, all users can improve access times for all users significantly.

Replication systems vary. Therefore, it is important to distinguish which type is a good fit for your organizational infrastructure needs and business goals. There are three main types of data replication systems:

Transactional Replication

Transaction replication consists of databases being copied in their entirety from the primary server (the publisher) and sent to secondary servers (subscribers). Any data changes are consistently and continuously updated. Transactional consistency is ensured, which means that data is replicated in real-time and sent from the primary server to secondary servers in the order of their occurrence. As a result, transactional replication makes it easy to track changes and any lost data. This type of replication is commonly used in server-to-server environments.

Snapshot Replication

In the snapshot replication type, a snapshot of the database is distributed from the primary server to the secondary servers. Instead of continuous updates, data is sent as it exists at the time of the snapshot. It is recommended to use this type of replication when there are not many data changes or at the initial synchronization between the publisher and subscriber.

Merge Replication

A merge replication consists of two databases being combined into a single database. As a result, any changes to data can be updated from the publisher to the subscribers. This is a complex type of replication since both parties (the primary server and the secondary servers) can make changes to the data. It is recommended to use this type of replication in a server-to-client environment.

Replication schemes are the operations and tasks required to perform replication. There are three main replication schemes organizations can choose from:

Full Replication

Full replication occurs when the entire database is copied in its entirety to every site in the distributed system. This scheme improves data availability and accessibility through database redundancy. In addition, performance is improved because global distribution of data reduces latency and accelerates query execution. On the other hand, it is difficult to achieve concurrency and update processes are slow.

Partial Replication

In a partial replication scheme, some sections of the database are replicated across some or all of the sites. The description of these fragments can be found in the replication schema. Partial replication enables prioritizing which data is important and should be replicated as well as distributing resources according to the needs of the field.

No Replication

In this scheme, data is stored on one site only. This enables easily recovering data and achieving concurrency. On the other hand, it negatively impacts availability and performance.

Replicating data can take place through different techniques. These include:

Full-table Replication

In a full-table replication, all data is copied from the source to the destination. This includes new data, as well as existing data. It is recommended to use this technique if records are regularly deleted or if other techniques are technically impossible. On the other hand, this technique requires more processing and network resources and the cost is higher.

Key-based Replication

In a key-based replication, only new data that has been added since the previous update, is updated. This technique is more efficient since less rows are copied. On the other hand, it does not enable replication data from a previous update that might have been hard-deleted.

Log-based Replication

A log-based replication replicates any changes to the database, from the DB log file. It applies only to database sources and has to be supported by it. This technique is recommended when the source database structure is static, otherwise it might become a very resource-intensive process.

When organizations digitally transform their infrastructure and migrate to the cloud, data can be replicated to cloud instances. By replicating data to the cloud, organizations can enjoy its benefits: scalability, global accessibility, data availability and easier maintenance. This means organizational users benefit from data that is more accessible, usable and reliable, which eliminates internal silos and increases business agility.

When replicating data to the cloud, it is important to monitor the process. The growing complexity of data systems as well as the increased physical distance between servers within a system could pose some risks.

These risks include:

Data schema and data profiling anomalies, like null counts, type changes and skew.

Ensuring all data has been migrated from the sources to the instances.

Data not being successfully migrated on time.

By implementing a management system to oversee and monitor the replications process, organizations can significantly reduce the risks involved in the data replication process. A data observability platform will ensure:

• Data is successfully replicated to other instances, including cloud instances
• Replication and migration pipelines are performing as expected
• Any broken pipelines or irregular data volumes are alerted about so they can be fixed
• Data is delivered on time
• Delivered data is reliable, so organizational stakeholders can use it for analytics

By monitoring the data pipelines that take part in the replication process, organizations and their DataOps engineer can ensure the data propagated through the pipeline is accurate, complete and reliable. This ensures data replicated to all instances can be reliably used by stakeholders. An effective monitoring system will be:

• Granular – specifically indicating where the issue is
• Persistent – following lineage to understand where errors began
• Automated – reducing manual errors and enabling the use of thresholds
• Ubiquitous – covering the pipeline end-to-end
• Timely – enabling catching errors on time before they have an impact

Learn more about data monitoring here.

Tracking pipelines enables systematic troubleshooting, so that any errors are identified and fixed on time. This ensures users constantly benefit from updated, reliable and healthy data in their analyses. There are various types of metadata that can be tracked, like task duration, task status, when data was updated, and more. By tracking and alerting (see below) in case of anomalies, DataOps engineers ensure data health.

Alerting about and data pipeline anomalies is an essential step that closes the observability loop. Alerting DataOps engineers gives them the opportunity to fix any data health issues that might affect data replication across various instances.

Within existing data systems, data engineers can trigger alerts for:

• Missed data deliveries
• Schema changes that are unexpected
• SLA misses
• anomalies in column-level statistics like nulls and distributions
• Irregular data volumes and sizes
• Pipeline failures, inefficiencies, and errors

By proactively setting up alerts and monitoring them through dashboards and other tools of your choice (Slack, Pagerduty, etc.), organizations can truly maximize the potential of data replication for their business.

Data replication holds great promise for organizations. By replicating data to multiple instances, they can ensure data availability and improved performance, as well as internal “insurance” in case of a disaster. This page covers the basics for any business or data engineer getting started with data replication: the variations, schemes and techniques, as well as more advanced content for monitoring the process to gain observability and reduce the potential risk.

Wherever you are on your data replication journey, we recommend auditing your pipelines to ensure data health. If you need help finding and fixing data health issues fast, click here.

A data catalog is an inventory of all of an organization’s data assets. A data catalog includes assets like machine learning models, structured data, unstructured data, data reports, and more. By leveraging data management tools, data analysts, data scientists, and other data users can search through the catalog, find the organizational data they need, and access it.

Governance of data assets in a data catalog is enabled through metadata. The metadata is used for mapping, describing, tagging, and organizing the data assets. As a result, it can be leveraged to enable data consumers to efficiently search through assets and get information on how to use the data. Metadata can also be used for augmenting data management, by enabling onboarding automation, anomalies alerts, auto-scaling, and more.
In addition to indexing the assets, a data catalog usually includes data access and data searching capabilities, as well as tools for enriching the metadata, both manually and automatically. It also provides capabilities for ensuring compliance with privacy regulations and security standards.
In modern organizations, data catalogs have become essential for leveraging the large amounts of data generated. Efficient data analysis and consumption can help organizations make better decisions, so they can optimize operations, build better models, increase sales, and more.

A data catalog provides multiple benefits to data professionals, business analysts, and organizations. These include:

User Autonomy

Data professionals and other data consumers can find data, evaluate it and understand how to use it – all on their own. With a data catalog, they no longer have to rely on IT or other professional personnel. Instead, they can immediately search for the data they need and use it. This speed and independence enable injecting data into more business operations. It also improves employee morale.

Improved Data Context and Quality

The metadata and comments on the data from other data citizens can help data consumers better understand how to use it. This additional information creates context and improves the data quality and encourages data usage, innovation, and more new business ideas.

Organizational Efficiency

Accessible data reduces operational friction and bottlenecks, like back and forth emails, which optimizes the use of organizational resources. Available data also accelerates internal processes. When data consumers get the data and understand how to use it faster, data analysis and implementation take place faster as well, benefiting the business.

Compliance and Security

Data catalogs that ensure data assets comply with privacy standards and security regulations, and reduce the risks of data breaches, cyberattacks, or legal fiascos.

New Business Opportunities

By giving data citizens new information they can incorporate into their work and decision-making, they will find new ways to answer work challenges and achieve their business goals. This can open up new business opportunities, across all departments.

Better Decision Making

Lack of data visibility makes organizations rely on tribal knowledge, rely on data they are already familiar with, or recreate assets that already exist. This creates organizational data silos, which impede productivity. Enabling data access to everyone improves the ability to find and use data consistently and continuously across the organization.

Different data catalogs offer somewhat different features. However, to enable data governance and advanced analysis, they should all provide the following to data consumers:

Metadata

Technical Metadata

The data that describes the structure of the objects, like tables, schemas, columns, rows, file names, etc.

Business Metadata

Data about the business value of the data, like its purpose, compliance info, rating, classification, etc.

Process Metadata

Data about the asset creation process and lineage, like who changed it and when, permissions, latest update time, etc.

Search Capabilities

Searching, browsing, and filtering options to enable data consumers to easily find the relevant data assets.

Metadata Enrichment

The ability to automatically enrich metadata through mappings and connections, as well as letting data citizens manually contribute to the metadata.

Compliance Capabilities

Embedded capabilities that ensure data can be trusted and no sensitive data is exposed. This is important for complying with regulations, standards, and policies.

Asset Connectivity

The ability to connect to and automatically map all types of data sources your organization uses, at the locations they reside at.

In addition, in technologically advanced and enterprise data catalogs, AI and machine learning are implemented.

Data catalogs can and should be consumed by all people in the organization. Some popular use cases include:

• Optimizing the data pipeline
• Data lake modernization
• Self-service analytics
• Cloud spend management
• Advanced analytics
• Reducing fraud risk
• Compliance audits
• And more

A data catalog can be used by data-savvy citizens, like data analysts, data scientists and data engineers. But all business employees – product, marketing, sales, customer success, etc – can work with data and benefit from a data catalog. Data catalogs are managed by data stewards.

Here are the top 10 data catalog tools according to G2, as of Q1 2022:

• Product Name: AWS Glue
• Product Description: AWS Glue is a serverless data integration service for discovering, preparing, and combining data for analytics, machine learning and application development. Data engineers and ETL developers can visually create, run, and monitor ETL workflows. Data analysts and data scientists can enrich, clean, and normalize data without writing code. Application developers can use familiar Structured Query Language (SQL) to combine and replicate data across different data stores.

• Product Name: Aginity
• Product Description: Aginity provides a SQL coding solution for data analysts, data engineers, and data scientists so they can find, manage, govern, share and re-use SQL rather than recode it.

• Product Name: Alation Data Catalog
• Product Description: ​​Alation’s data catalog indexes a wide variety of data sources, including relational databases, cloud data lakes, and file systems using machine learning. Alation enables company-wide access to data and also surfaces recommendations, flags, and policies as data consumers query in a built-in SQL editor or search using natural language. Alation connects to a wide range of popular data sources and BI tools through APIs and an Open Connector SDK to streamline analytics.

• Product Name: Collibra Data Catalog
• Product Description: Collibra ensures teams can quickly find, understand and access data across sources, business applications, BI, and data science tools in one central location. Features include out-of-the-box integrations for common data sources, business applications, BI and data science tools; machine learning-powered automation capabilities; automated relationship mapping; and data governance and privacy capabilities.

• Product Name: IBM Watson Knowledge Catalog
• Product Description:  A data catalog tool based on self-service discovery of data, models and more. The cloud-based enterprise metadata repository activates information for AI, machine learning (ML), and deep learning. IBM’s data catalog enables stakeholders to access, curate, categorize and share data, knowledge assets and their relationships, wherever they reside.

• Product Name: Appen
• Product Description: Appen provides a licensable data annotation platform for training data use cases in computer vision and natural language processing. In order to create training data, Appen collects and labels images, text, speech, audio, video, and other data. Its Smart Labeling and Pre-Labeling features that use Machine Learning ease human annotations.

• Product Name: Denodo
• Product Description: Denodo provides data virtualization that enables access to the cloud, big data, and unstructured data sources in their original repositories. Denodo enables the building of customized data models for customers and supports multiple viewing formats.

• Product Name: Oracle Enterprise Metadata Management 
• Product Description: Oracle Enterprise Metadata Management harvests metadata from Oracle and third-party data integrations, business intelligence, ETL, big data, database, and data warehousing technologies. It enables business reporting, versioning, and comparison of metadata models, metadata search and browsing, and data lineage and impact analysis reports.

• Product Name: Unifi Data Catalog
• Product Description: A standalone Data Catalog with intuitive natural language search powered by AI, collaboration capabilities for crowd-sourced data quality, views of trusted data, and all fully governed by IT. The Unifi Data Catalog offers data source cataloging, search and discovery capabilities throughout all data locations and structures, auto-generated recommendations to view and explore data sets and similar data sets, integration to catalog Tableau metadata, and the ability to deconstruct TWBX files and see the full lineage of a data source to see how data sets were transformed.

• Product Name: Catalog Manager for IMS
• Product Description: A system database that stores metadata about databases and applications. Catalog Manager for IMS enables viewing IMS catalog content, reporting on the control block information in the IMS catalog, and creating jobs to do DBDGENs, PSBGENs, and ACBGENs to populate the catalog.

A data catalog can organize and govern data that reside in repositories, data lakes, data warehouses, or other locations. A data catalog can help organize the unstructured data in the data lake, preventing it from turning into a “data swamp”. As a result, data scientists and data analysts can easily pull data from the lake, evaluate it and use it.

Databand is a proactive observability platform for monitoring and controlling data quality, as early as ingestion. By integrating Databand with your data catalog, you can gain extended lineage, and visualize and observe the data from its source and as it flows through the pipelines all the way to the assets the data catalog maps and governs. As a result, data scientists, engineers and other data professionals can see and understand the complete flow of data, end-to-end.

In addition, by integrating Databand with your data catalog, you can get proactive alerts any time your data quality is affected to increase governance and robustness. This is enabled through Databand’s data quality identification capabilities, combined with how data catalogs map assets to owners. Databand will communicate any data quality issues to the relevant data owners.

A modern data platform should provide a complete solution for the processing, analyzing, and presentation of data. It is built as a cloud-first, cloud-native platform, and, normally, can be set up within a few hours. A modern data platform is supported not only by technology, but also by the Agile, DevOps, and DataOps philosophies.

Currently, data lakes and data warehouses are popular storage systems, but each comes with some limitations.

Data lakehouses and data mesh storage systems are two new systems attempting to overcome those limitations, and are showing signs of gaining popularity.

The modern data platform typically includes six foundational layers guided by principles of elasticity and availability.

DevOps and DataOps have two entirely different purposes, but both are similar to the Agile philosophy, which is designed to accelerate project work cycles.

DevOps is focused on product development, while DataOps focuses on creating and maintaining a distributed data architecture system with the goal of creating business value from data.

Agile is a philosophy for software development that promotes speed and efficiency, but without eliminating the “human” factor. It places an emphasis on face-to-face conversations as a way to maximize communications and emphasizes automation as a way to minimize errors.

The process of placing data into a storage system for future use is called data ingestion. In simple terms, data ingestion means moving data taken from other sources to a central location. From there the data can be used for record-keeping purposes, or for further processing and analysis. Both analytics systems and downstream reporting rely on accessible, consistent, and accurate data.

Organizations make business decisions using the data from their analytics infrastructure. The value of their data is dependent on how well it is ingested and integrated. If there are problems during the ingestion process, such as missing data, every step of the analytics process will suffer.

Ingesting data can be done in different ways, and the way a particular data ingestion layer is designed can be based on different processing models. Data can come from a variety of distinct sources, ranging from SaaS platforms to the internet of things to mobile devices. A good ingestion model acts as a foundation for an efficient data strategy, and organizations normally choose the model best-suited for the circumstances.

Batch processing is the most common form of data ingestion. But it is not designed to deal with customers in real time. Instead it collects and groups source data into batches, which are sent  to the destination.

Batch processing may be initiated using a simple schedule, or it may be activated when certain conditions exist.  It is often used when the use of real-time data is not needed, as it is usually easier and less expensive than streaming ingestion.

Real-time processing (also referred to as streaming or stream processing) does not group data. Instead, data is obtained, transformed, and loaded as soon as it is recognized. Real-time processing is more expensive because it requires constant monitoring of data sources and accepts new information, automatically.

Modern data ingestion models, until recently, used an ETL (extract, transform, load procedure) to take data from its source, reformatting it, and then transporting it to its destination. This made sense when businesses had to use expensive in-house analytics systems, and doing the prep work before delivering it, including transformations, lowered costs.

That situation has changed, and more updated cloud data warehouses (Snowflake, Google BigQuery, Microsoft Azure, and others) can now cost-effectively scale their computing and storage resources. These improvements allow the preload transformation steps to be dropped, with raw data being delivered to the data warehouse.

At this point, the data can be translated into an SQL format, and then run within the data warehouse during research. This new processing arrangement has changed ETL to ELT (extract, load, transform).

Instead of extracting the data and then transforming it, with ELT data is transformed “after” it is in the cloud’s data warehouse.

Data transformation deals with changing the values, structure, and format of data. This is often necessary for data analytics projects. Data can be transformed during one of two stages when using a data pipeline, before arriving at its storage destination, or after. Organizations still using on-premises data warehouses will normally use an ETL process.

Today, many organizations are using cloud-based data warehouses. These can scale computing and storage resources as needed. The ability of the cloud to scale allows businesses to bypass the preload transformations and send raw data into the data warehouse. The data is transformed after arriving, using an ELT process, typically when answering a query.

There are various advantages to transforming data:

• Usability – Too many organizations sit on a bunch of unusable, unanalyzed data. Standardizing data and putting it under the right structure allows your data team to generate business value out of it.

• Data quality – Transforming raw data can lead to missing values, poorly formatted variables, null rows, etcetera. (It is also possible to use data transformation to “improve” data quality.)
• Better organization – transformed data is easier to process for both people and computers

Currently, The two most popular storage formats are data warehouses and data lakes. And then there are two storage formats that are gaining in popularity — the data lakehouse and data mesh. Modern data storage systems are focused on using data efficiently.

Cloud-based data warehouses have been the preferred data storage system for a number of years because they can optimize computing power and processing speeds. They were developed much earlier than data lakes and can be traced back to the 1990s when databases were used for storage. The early versions of data warehouses were in-house and had very limited storage capacity. In 2013, many data warehouses shifted to the cloud and gained scalable storage.

Data lakes were originally built on Hadoop, were scalable, and were designed for on-premises use. In January of 2008, Yahoo released Hadoop (based on NoSQL) as an open-source project to the Apache Software Foundation. Unfortunately, the Hadoop ecosystem is extremely complex and difficult to work with. Data Lakes began shifting to the cloud around 2015, making them much less expensive, and much more user-friendly.

Using a combination of data lakes and data warehouses to minimize their limitations has become a common practice.

Data lakes have problems with “parsing data.” They were originally designed to collect data in its natural format, without enforcing schema (formats), so that researchers could gain more insights from a broad range of data. Unfortunately, data lakes can become data swamps, with old, inaccurate information and useless information, making them much less effective.

Data warehouses are designed for managing structured data with clear and defined use cases.

For the data warehouse to function properly, the data must be collected, reformatted, cleaned, and uploaded to the warehouse. Some data, which cannot be reformatted, may be lost.

The data lakehouse has been designed to merge the strengths of data warehouses and lakes.

Data lakehouses are a new form of data management architecture. They merge the flexibility, cost-efficiency, and scaling abilities of data lakes with the ACID transactions and data management features of data warehouses.

Data lakehouses support business intelligence and machine learning. One of the data lakehouse’s strengths is its use of metadata layers. It also uses a new query engine, designed for high-performance SQL searches.

Data mesh can be quite useful for organizations that are expanding quickly and need scalability for their data storage.

Data mesh, unlike data warehouses, lakes, and lakehouses, is “decentralized.” Decentralized data ownership is an architectural model where a specific domain (business partners or other departments) does not own their data, but shares data freely with other domains.

Data is not owned in the data mesh model. It is not owned by the people storing it — but they are responsible for it. The data is stored and organized by the business partner or department, with the knowledge the data is to be shared. This means all data within the data mesh system should maintain a uniform format.

Data mesh systems can be useful for businesses supporting multiple data domains. Within the data mesh design, there is a data governance layer and a layer of observability. There is also a universal interoperability layer.

Data observability has recently become a hot topic. Data observability describes the ability to watch and observe the state of data and its health. It covers a number of activities and technologies that, when combined, allow the user to identify and resolve data difficulties in near real-time.

Data observability platforms can be used with data warehouses, data lakes, data lakehouses, and data mesh.

It should be noted Databand has developed what is called a proactive data observability platform capable of catching bad data before it causes damage.

Observability allows teams to answer specific questions about what is taking place behind the scene in extremely distributed systems. Observability can show where data is moving slowly and what is broken.

Managers and/or teams can be sent alerts about potential problems and pro-actively solve them. (While the predictability feature can be helpful, it will not catch all problems, nor should it be expected to. Think of problem predictions as helpful, but not a guarantee.)

To make data observability useful, it needs to include these features:

• SLA Tracking – This feature measures pipeline metadata and data quality against pre-defined standards.
• Monitoring – A dashboard is provided, showing the operations of your system or pipeline.
• Logging – Historical records (tracking, comparisons, analysis) of events are kept for comparison with newly discovered anomalies.
• Alerting – Warnings are sent out for both anomalies and expected events.
• Analysis – An automated detection process that adapts to your system.
• Tracking –  Offers the ability to track specific events.
• Comparisons – Provides a historical background, and anomaly alerts.

For many organizations, observability is siloed, meaning only certain departments can access the data. (This “should not” happen in a data mesh system, which philosophically requires the data to be shared, and is generally discouraged in most storage and processing systems.) Teams collect metadata on the pipelines they own.

In 1865, the phrase ‘Business Intelligence’ was used in the Cyclopædia of Commercial and Business Anecdotes. This described how Sir Henry Furnese (who was a banker) profited from the information he gathered, and how he used it before his competition.

Currently, a great deal of business information is gathered from business analytics, as well as data analytics. Analytics is used to generate business intelligence by transforming data into understandable insights which can help to make tactical and strategic business decisions. Business intelligence tools can be used to access and analyze data, providing researchers with detailed intelligence.

Data discovery involves collecting and evaluating data from different sources. It is often used to gain an understanding of the trends and patterns found in the data. Data discovery is sometimes associated with business intelligence because it can bring together siloed data for analysis.

Data discovery includes connecting a variety of data sources. It can clean and prepare data, and perform analytics. Inaccessible data is essentially useless data, and data discovery makes it useful.

Data discovery is about exploring data with visual tools which can help business leaders detect new patterns and anomalies.

If you search for “Modern Data Platform Trends” in Google, you’ll see many articles discussing trends on what’s next for the data platform. Topics like metadata management, building a metrics layer, and reverse ETL are getting a lot of focus.

However, the trend of data observability seems universally pervasive in all these articles. Data-driven companies can’t afford to constantly question whether or not the data they consume is reliable and trustworthy.

Data stacks enable data integration throughout the entire data pipeline for trustworthy consumption. But how can companies ensure their data stack is both modern and reliable? In this blog post, we discuss these issues, as well as how GCP manages its data stack.

This blog post is based on a podcast where we hosted Sudhir Hasbe, senior director of product management for all data analytics services at Google Cloud.

You can listen to the entire episode below or here.

In today’s world, we have the capacity and ability to track almost any piece of data. But attempting to find relevant information in such huge volumes of data is not always so easy to do. A data stack is a tool suite for data integration. It transforms or loads data into a data warehouse, enables transformation through an engine running on top and provides visibility for building applications.

As companies evolve, they often move towards a modern data stack that is based on a cloud-based warehouse. Such a stack enables real-time, personalized experiences or predictions on SaaS applications by supporting real-time events and decision-making.

To support real-time events, modern data stacks include the following components:

• A system for collecting events, like Kafka
• A processing system, like a streaming analytics systems or a Spark streaming solution
• A serving layer
• A data lake or staging environment where raw data can be pushed and transformed before it gets loaded into a data warehouse
• A data warehouse for structured data that enables creating machine learning models

Such an environment is very complex and requires controls to ensure high data quality. Otherwise, bad data will be pulled into different systems and create a poor customer experience.

Therefore, it is important to ensure data quality is taken into consideration as early as the design phase of the data stack.

As companies rely on more and more data sources, managing them becomes more complex. Therefore, to ensure data quality throughout the entire pipeline, it becomes important to understand the source of issues, i.e where they are originally coming from.

Data engineers who only look at the tables or the dashboards downstream will be wasting a lot of time trying to find where issues are coming from. They might be able to catch the problem, but by tracing them back to the source they will be able to debug and solve the issue much more quickly.

By shifting left observability requirements, data engineers can ensure data quality as early as ingestion, and enable much higher delivery velocity.

One of the main pain points data teams have when building and operationalizing a data stack is how to ensure delivery velocity.

This is true for both on-prem and cloud-native stacks, but becomes more pressing when companies are required to support real-time events both quickly and with high data quality.

To ensure delivery velocity at GCP, the team implements the following solutions.

End-to-end Pipelines

At GCP, one of the most critical components in the data stack is end-to-end pipelines. Thanks to these pipelines, they can ensure real-time events from various sources are available in their data warehouse, BigQuery. To cover streaming analytics use cases, data is seamlessly connected to BigTable and DataFlow.

Consistent Storage

At GCP, all storage capabilities are unified and consistent in a single data lake, enabling different types of processing on top of it. Thus, each persona can use their own skills and tools to consume it.

For example, a data engineer could use Java or Python, a data scientist could use notebooks or TensorFlow and an analyst could use other tools to analyze the data.

Here are three interesting predictions regarding the future of data stacks.

Leverage AI and ML to Improve Data Quality

One of the most interesting ideas when discussing the future of data management is about implementing AI and ML to improve data quality. Often, machine learning is used to improve business metrics.

At GCP, the team is implementing ML on BigQuery to identify failures. They find it is the only way to detect issues at scale. While this practice hasn’t been widely adopted by many companies, yet, it is expected to in the future.

Less Manual, More Automation

Automation is predicted to be widely adopted, as a means for managing the huge volumes of data of the future.

Today, manual management of legacy data platforms is complex, since it is based on components like Hadoop Spark running on a data warehouse with manually-defined rules and integrations with Jira to enable more personas to run queries.

The result is often hundreds of tickets with false alarms. In the future, automation will cover:

• Metric collection
• Alerts (without having to manually define rules)
• New data sources and products

It will include automatic data discovery through automated cataloging of all information, including centralized metadata, automated lineage tracking across all systems and metadata lineage.

This will reduce the number of errors and streamline the process.

Persona Changes are Coming

Finally, we predict that the personas who process and consume the data will change as well. Data consumption will not be limited to data engineers, data scientists and analysts, but will be open to all business employees.

As a result, in the future, storage will just be a price-performance discussion for customers rather than capability differentiation.

The future sounds bright! Databand provides data engineers with observability into data sources straight from the source. To get a free trial, click here.